Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm
This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for...
Ausführliche Beschreibung
Autor*in: |
Yuan, Zhi [verfasserIn] Wang, Weiqing [verfasserIn] Wang, Haiyun [verfasserIn] Ashourian, Mohsen [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020 |
---|
Rechteinformationen: |
Open Access Namensnennung 4.0 International ; CC BY 4.0 |
---|
Übergeordnetes Werk: |
Enthalten in: Energy reports - Amsterdam [u.a.] : Elsevier, 2015, 6(2020) vom: Nov., Seite 1572-1580 |
---|---|
Übergeordnetes Werk: |
volume:6 ; year:2020 ; month:11 ; pages:1572-1580 |
Links: |
---|
DOI / URN: |
10.1016/j.egyr.2020.06.011 |
---|
Katalog-ID: |
172858731X |
---|
LEADER | 01000caa a2200265 4500 | ||
---|---|---|---|
001 | 172858731X | ||
003 | DE-627 | ||
005 | 20220121160415.0 | ||
007 | cr uuu---uuuuu | ||
008 | 200902s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.egyr.2020.06.011 |2 doi | |
024 | 7 | |a 10419/244146 |2 hdl | |
035 | |a (DE-627)172858731X | ||
035 | |a (DE-599)KXP172858731X | ||
040 | |a DE-627 |b ger |c DE-627 |e rda | ||
041 | |a eng | ||
100 | 1 | |a Yuan, Zhi |e verfasserin |0 (DE-588)1207951595 |0 (DE-627)1694291065 |4 aut | |
245 | 1 | 0 | |a Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm |c Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
506 | 0 | |q DE-206 |a Open Access |e Controlled Vocabulary for Access Rights |u http://purl.org/coar/access_right/c_abf2 | |
520 | |a This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. | ||
540 | |q DE-206 |a Namensnennung 4.0 International |f CC BY 4.0 |2 cc |u https://creativecommons.org/licenses/by/4.0/ | ||
700 | 1 | |a Wang, Weiqing |e verfasserin |0 (DE-588)1207951846 |0 (DE-627)1694291766 |4 aut | |
700 | 1 | |a Wang, Haiyun |e verfasserin |0 (DE-588)1207951951 |0 (DE-627)1694291871 |4 aut | |
700 | 1 | |a Ashourian, Mohsen |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Energy reports |d Amsterdam [u.a.] : Elsevier, 2015 |g 6(2020) vom: Nov., Seite 1572-1580 |h Online-Ressource |w (DE-627)820689033 |w (DE-600)2814795-9 |w (DE-576)427950821 |x 2352-4847 |7 nnns |
773 | 1 | 8 | |g volume:6 |g year:2020 |g month:11 |g pages:1572-1580 |
856 | 4 | 0 | |u https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf |x Verlag |z kostenfrei |
856 | 4 | 0 | |u https://doi.org/10.1016/j.egyr.2020.06.011 |x Resolving-System |z kostenfrei |
856 | 4 | 0 | |u http://hdl.handle.net/10419/244146 |x Resolving-System |z kostenfrei |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ILN_26 | ||
912 | |a ISIL_DE-206 | ||
912 | |a SYSFLAG_1 | ||
912 | |a GBV_KXP | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_224 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2007 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2026 | ||
912 | |a GBV_ILN_2027 | ||
912 | |a GBV_ILN_2034 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2049 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2059 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2064 | ||
912 | |a GBV_ILN_2088 | ||
912 | |a GBV_ILN_2106 | ||
912 | |a GBV_ILN_2110 | ||
912 | |a GBV_ILN_2112 | ||
912 | |a GBV_ILN_2122 | ||
912 | |a GBV_ILN_2129 | ||
912 | |a GBV_ILN_2143 | ||
912 | |a GBV_ILN_2152 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_2232 | ||
912 | |a GBV_ILN_2336 | ||
912 | |a GBV_ILN_2470 | ||
912 | |a GBV_ILN_2507 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4035 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4242 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4251 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4326 | ||
912 | |a GBV_ILN_4333 | ||
912 | |a GBV_ILN_4334 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4393 | ||
912 | |a GBV_ILN_4700 | ||
912 | |a GBV_ILN_2403 | ||
912 | |a GBV_ILN_2403 | ||
912 | |a ISIL_DE-LFER | ||
951 | |a AR | ||
952 | |d 6 |j 2020 |c 11 |h 1572-1580 | ||
980 | |2 26 |1 01 |x 0206 |b 374597865X |y x1z |z 02-09-20 | ||
980 | |2 2403 |1 01 |x DE-LFER |b 3758605504 |c 00 |f --%%-- |d --%%-- |e n |j --%%-- |y l01 |z 18-09-20 | ||
981 | |2 2403 |1 01 |x DE-LFER |r https://doi.org/10.1016/j.egyr.2020.06.011 | ||
981 | |2 2403 |1 01 |x DE-LFER |r https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a Convolutional neural network | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a Deer hunting optimization algorithm | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a Parameter identification | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a Proton exchange membrane fuel cell |
author_variant |
z y zy w w ww h w hw m a ma |
---|---|
matchkey_str |
article:23524847:2020----::aaeeietfctoopmcaeocnouinlerlewrotmzdyaacde |
hierarchy_sort_str |
2020 |
publishDate |
2020 |
allfields |
10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell |
spelling |
10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell |
allfields_unstemmed |
10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell |
allfieldsGer |
10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell |
allfieldsSound |
10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell |
language |
English |
source |
Enthalten in Energy reports 6(2020) vom: Nov., Seite 1572-1580 volume:6 year:2020 month:11 pages:1572-1580 |
sourceStr |
Enthalten in Energy reports 6(2020) vom: Nov., Seite 1572-1580 volume:6 year:2020 month:11 pages:1572-1580 |
format_phy_str_mv |
Article |
building |
26:1 2403:0 |
institution |
findex.gbv.de |
selectbib_iln_str_mv |
26@1z 2403@01 |
sw_local_iln_str_mv |
26:Convolutional neural network DE-206:Convolutional neural network 26:Deer hunting optimization algorithm DE-206:Deer hunting optimization algorithm 26:Parameter identification DE-206:Parameter identification 26:Proton exchange membrane fuel cell DE-206:Proton exchange membrane fuel cell |
isfreeaccess_bool |
true |
container_title |
Energy reports |
authorswithroles_txt_mv |
Yuan, Zhi @@aut@@ Wang, Weiqing @@aut@@ Wang, Haiyun @@aut@@ Ashourian, Mohsen @@aut@@ |
publishDateDaySort_date |
2020-11-01T00:00:00Z |
hierarchy_top_id |
820689033 |
id |
172858731X |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">172858731X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220121160415.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">200902s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.egyr.2020.06.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/244146</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)172858731X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP172858731X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yuan, Zhi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1207951595</subfield><subfield code="0">(DE-627)1694291065</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm</subfield><subfield code="c">Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Namensnennung 4.0 International</subfield><subfield code="f">CC BY 4.0</subfield><subfield code="2">cc</subfield><subfield code="u">https://creativecommons.org/licenses/by/4.0/</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Weiqing</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1207951846</subfield><subfield code="0">(DE-627)1694291766</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Haiyun</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1207951951</subfield><subfield code="0">(DE-627)1694291871</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ashourian, Mohsen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Energy reports</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 2015</subfield><subfield code="g">6(2020) vom: Nov., Seite 1572-1580</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)820689033</subfield><subfield code="w">(DE-600)2814795-9</subfield><subfield code="w">(DE-576)427950821</subfield><subfield code="x">2352-4847</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2020</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1572-1580</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.egyr.2020.06.011</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://hdl.handle.net/10419/244146</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2020</subfield><subfield code="c">11</subfield><subfield code="h">1572-1580</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">374597865X</subfield><subfield code="y">x1z</subfield><subfield code="z">02-09-20</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">3758605504</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">18-09-20</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1016/j.egyr.2020.06.011</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Convolutional neural network</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Deer hunting optimization algorithm</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Parameter identification</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Proton exchange membrane fuel cell</subfield></datafield></record></collection>
|
standort_str_mv |
--%%-- |
standort_iln_str_mv |
2403:--%%-- DE-LFER:--%%-- |
author |
Yuan, Zhi |
spellingShingle |
Yuan, Zhi 26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm |
authorStr |
Yuan, Zhi |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)820689033 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
typewithnormlink_str_mv |
DifferentiatedPerson@(DE-588)1207951595 Person@(DE-588)1207951595 Person@(DE-588)1207951846 DifferentiatedPerson@(DE-588)1207951846 DifferentiatedPerson@(DE-588)1207951951 Person@(DE-588)1207951951 |
collection |
KXP GVK SWB |
remote_str |
true |
last_changed_iln_str_mv |
26@02-09-20 2403@18-09-20 |
illustrated |
Not Illustrated |
issn |
2352-4847 |
topic_title |
26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian |
topic |
26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell |
topic_unstemmed |
26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell |
topic_browse |
26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
standort_txtP_mv |
--%%-- |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Energy reports |
normlinkwithtype_str_mv |
(DE-588)1207951595@DifferentiatedPerson (DE-588)1207951595@Person (DE-588)1207951846@Person (DE-588)1207951846@DifferentiatedPerson (DE-588)1207951951@DifferentiatedPerson (DE-588)1207951951@Person |
hierarchy_parent_id |
820689033 |
signature |
--%%-- |
signature_str_mv |
--%%-- |
hierarchy_top_title |
Energy reports |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 |
normlinkwithrole_str_mv |
(DE-588)1207951595@@aut@@ (DE-588)1207951846@@aut@@ (DE-588)1207951951@@aut@@ |
title |
Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm |
ctrlnum |
(DE-627)172858731X (DE-599)KXP172858731X |
title_full |
Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian |
author_sort |
Yuan, Zhi |
journal |
Energy reports |
journalStr |
Energy reports |
callnumber-first-code |
- |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
txt |
container_start_page |
1572 |
author_browse |
Yuan, Zhi Wang, Weiqing Wang, Haiyun Ashourian, Mohsen |
selectkey |
26:x 2403:l |
container_volume |
6 |
format_se |
Elektronische Aufsätze |
author-letter |
Yuan, Zhi |
doi_str_mv |
10.1016/j.egyr.2020.06.011 |
normlink |
1207951595 1694291065 1207951846 1694291766 1207951951 1694291871 |
normlink_prefix_str_mv |
(DE-588)1207951595 (DE-627)1694291065 (DE-588)1207951846 (DE-627)1694291766 (DE-588)1207951951 (DE-627)1694291871 |
author2-role |
verfasserin |
title_sort |
parameter identification of pemfc based on convolutional neural network optimized by balanced deer hunting optimization algorithm |
title_auth |
Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm |
abstract |
This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. |
abstractGer |
This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. |
abstract_unstemmed |
This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. |
collection_details |
GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 ISIL_DE-LFER |
title_short |
Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm |
url |
https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf https://doi.org/10.1016/j.egyr.2020.06.011 http://hdl.handle.net/10419/244146 |
ausleihindikator_str_mv |
26 2403:n |
rolewithnormlink_str_mv |
@@aut@@(DE-588)1207951595 @@aut@@(DE-588)1207951846 @@aut@@(DE-588)1207951951 |
remote_bool |
true |
author2 |
Wang, Weiqing Wang, Haiyun Ashourian, Mohsen |
author2Str |
Wang, Weiqing Wang, Haiyun Ashourian, Mohsen |
ppnlink |
820689033 |
GND_str_mv |
Zhi, Yuan Yuan Zhi Yuan, Zhi Weiqing, Wang Wang Weiqing Wang, Weiqing Haiyun, Wang Wang Haiyun Wang, Haiyun |
GND_txt_mv |
Zhi, Yuan Yuan Zhi Yuan, Zhi Weiqing, Wang Wang Weiqing Wang, Weiqing Haiyun, Wang Wang Haiyun Wang, Haiyun |
GND_txtF_mv |
Zhi, Yuan Yuan Zhi Yuan, Zhi Weiqing, Wang Wang Weiqing Wang, Weiqing Haiyun, Wang Wang Haiyun Wang, Haiyun |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1016/j.egyr.2020.06.011 |
callnumber-a |
--%%-- |
up_date |
2024-07-05T02:29:27.182Z |
_version_ |
1803704406832578560 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">172858731X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220121160415.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">200902s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.egyr.2020.06.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/244146</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)172858731X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP172858731X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yuan, Zhi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1207951595</subfield><subfield code="0">(DE-627)1694291065</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm</subfield><subfield code="c">Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Namensnennung 4.0 International</subfield><subfield code="f">CC BY 4.0</subfield><subfield code="2">cc</subfield><subfield code="u">https://creativecommons.org/licenses/by/4.0/</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Weiqing</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1207951846</subfield><subfield code="0">(DE-627)1694291766</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Haiyun</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1207951951</subfield><subfield code="0">(DE-627)1694291871</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ashourian, Mohsen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Energy reports</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 2015</subfield><subfield code="g">6(2020) vom: Nov., Seite 1572-1580</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)820689033</subfield><subfield code="w">(DE-600)2814795-9</subfield><subfield code="w">(DE-576)427950821</subfield><subfield code="x">2352-4847</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2020</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1572-1580</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.egyr.2020.06.011</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://hdl.handle.net/10419/244146</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2020</subfield><subfield code="c">11</subfield><subfield code="h">1572-1580</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">374597865X</subfield><subfield code="y">x1z</subfield><subfield code="z">02-09-20</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">3758605504</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">18-09-20</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1016/j.egyr.2020.06.011</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Convolutional neural network</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Deer hunting optimization algorithm</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Parameter identification</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">Proton exchange membrane fuel cell</subfield></datafield></record></collection>
|
score |
7.4014473 |