A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application
Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Xiaoxiao [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 53(2021), 2 vom: 19. Feb., Seite 1287-1304 |
|---|---|
| Übergeordnetes Werk: |
volume:53 ; year:2021 ; number:2 ; day:19 ; month:02 ; pages:1287-1304 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11063-021-10440-x |
|---|
| Katalog-ID: |
OLC2124898833 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2124898833 | ||
| 003 | DE-627 | ||
| 005 | 20230505093626.0 | ||
| 007 | tu | ||
| 008 | 230505s2021 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11063-021-10440-x |2 doi | |
| 035 | |a (DE-627)OLC2124898833 | ||
| 035 | |a (DE-He213)s11063-021-10440-x-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 000 |q VZ |
| 100 | 1 | |a Li, Xiaoxiao |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 | ||
| 520 | |a Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. | ||
| 650 | 4 | |a Zhang neural network | |
| 650 | 4 | |a Varying-parameter convergence-accelerated neural network | |
| 650 | 4 | |a Noise-resistant | |
| 650 | 4 | |a Dynamic matrix pseudoinverse | |
| 700 | 1 | |a Li, Shuai |4 aut | |
| 700 | 1 | |a Xu, Zhihao |4 aut | |
| 700 | 1 | |a Zhou, Xuefeng |0 (orcid)0000-0003-1642-2059 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neural processing letters |d Springer US, 1994 |g 53(2021), 2 vom: 19. Feb., Seite 1287-1304 |w (DE-627)198692617 |w (DE-600)1316823-X |w (DE-576)052842762 |x 1370-4621 |7 nnns |
| 773 | 1 | 8 | |g volume:53 |g year:2021 |g number:2 |g day:19 |g month:02 |g pages:1287-1304 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11063-021-10440-x |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-PSY | ||
| 912 | |a SSG-OLC-MAT | ||
| 951 | |a AR | ||
| 952 | |d 53 |j 2021 |e 2 |b 19 |c 02 |h 1287-1304 | ||
| author_variant |
x l xl s l sl z x zx x z xz |
|---|---|
| matchkey_str |
article:13704621:2021----::vrprmtrovrecaclrtdeurnnuantokoolnsligyaimtipe |
| hierarchy_sort_str |
2021 |
| publishDate |
2021 |
| allfields |
10.1007/s11063-021-10440-x doi (DE-627)OLC2124898833 (DE-He213)s11063-021-10440-x-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Xiaoxiao verfasserin aut A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. Zhang neural network Varying-parameter convergence-accelerated neural network Noise-resistant Dynamic matrix pseudoinverse Li, Shuai aut Xu, Zhihao aut Zhou, Xuefeng (orcid)0000-0003-1642-2059 aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 19. Feb., Seite 1287-1304 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:19 month:02 pages:1287-1304 https://doi.org/10.1007/s11063-021-10440-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 19 02 1287-1304 |
| spelling |
10.1007/s11063-021-10440-x doi (DE-627)OLC2124898833 (DE-He213)s11063-021-10440-x-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Xiaoxiao verfasserin aut A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. Zhang neural network Varying-parameter convergence-accelerated neural network Noise-resistant Dynamic matrix pseudoinverse Li, Shuai aut Xu, Zhihao aut Zhou, Xuefeng (orcid)0000-0003-1642-2059 aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 19. Feb., Seite 1287-1304 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:19 month:02 pages:1287-1304 https://doi.org/10.1007/s11063-021-10440-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 19 02 1287-1304 |
| allfields_unstemmed |
10.1007/s11063-021-10440-x doi (DE-627)OLC2124898833 (DE-He213)s11063-021-10440-x-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Xiaoxiao verfasserin aut A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. Zhang neural network Varying-parameter convergence-accelerated neural network Noise-resistant Dynamic matrix pseudoinverse Li, Shuai aut Xu, Zhihao aut Zhou, Xuefeng (orcid)0000-0003-1642-2059 aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 19. Feb., Seite 1287-1304 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:19 month:02 pages:1287-1304 https://doi.org/10.1007/s11063-021-10440-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 19 02 1287-1304 |
| allfieldsGer |
10.1007/s11063-021-10440-x doi (DE-627)OLC2124898833 (DE-He213)s11063-021-10440-x-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Xiaoxiao verfasserin aut A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. Zhang neural network Varying-parameter convergence-accelerated neural network Noise-resistant Dynamic matrix pseudoinverse Li, Shuai aut Xu, Zhihao aut Zhou, Xuefeng (orcid)0000-0003-1642-2059 aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 19. Feb., Seite 1287-1304 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:19 month:02 pages:1287-1304 https://doi.org/10.1007/s11063-021-10440-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 19 02 1287-1304 |
| allfieldsSound |
10.1007/s11063-021-10440-x doi (DE-627)OLC2124898833 (DE-He213)s11063-021-10440-x-p DE-627 ger DE-627 rakwb eng 000 VZ Li, Xiaoxiao verfasserin aut A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. Zhang neural network Varying-parameter convergence-accelerated neural network Noise-resistant Dynamic matrix pseudoinverse Li, Shuai aut Xu, Zhihao aut Zhou, Xuefeng (orcid)0000-0003-1642-2059 aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 19. Feb., Seite 1287-1304 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:19 month:02 pages:1287-1304 https://doi.org/10.1007/s11063-021-10440-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 19 02 1287-1304 |
| language |
English |
| source |
Enthalten in Neural processing letters 53(2021), 2 vom: 19. Feb., Seite 1287-1304 volume:53 year:2021 number:2 day:19 month:02 pages:1287-1304 |
| sourceStr |
Enthalten in Neural processing letters 53(2021), 2 vom: 19. Feb., Seite 1287-1304 volume:53 year:2021 number:2 day:19 month:02 pages:1287-1304 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Zhang neural network Varying-parameter convergence-accelerated neural network Noise-resistant Dynamic matrix pseudoinverse |
| dewey-raw |
000 |
| isfreeaccess_bool |
false |
| container_title |
Neural processing letters |
| authorswithroles_txt_mv |
Li, Xiaoxiao @@aut@@ Li, Shuai @@aut@@ Xu, Zhihao @@aut@@ Zhou, Xuefeng @@aut@@ |
| publishDateDaySort_date |
2021-02-19T00:00:00Z |
| hierarchy_top_id |
198692617 |
| dewey-sort |
0 |
| id |
OLC2124898833 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2124898833</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505093626.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-021-10440-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2124898833</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-021-10440-x-p</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Xiaoxiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Zhang neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Varying-parameter convergence-accelerated neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Noise-resistant</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic matrix pseudoinverse</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Shuai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Zhihao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Xuefeng</subfield><subfield code="0">(orcid)0000-0003-1642-2059</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural processing letters</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">53(2021), 2 vom: 19. Feb., Seite 1287-1304</subfield><subfield code="w">(DE-627)198692617</subfield><subfield code="w">(DE-600)1316823-X</subfield><subfield code="w">(DE-576)052842762</subfield><subfield code="x">1370-4621</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:53</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:2</subfield><subfield code="g">day:19</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:1287-1304</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11063-021-10440-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PSY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">53</subfield><subfield code="j">2021</subfield><subfield code="e">2</subfield><subfield code="b">19</subfield><subfield code="c">02</subfield><subfield code="h">1287-1304</subfield></datafield></record></collection>
|
| author |
Li, Xiaoxiao |
| spellingShingle |
Li, Xiaoxiao ddc 000 misc Zhang neural network misc Varying-parameter convergence-accelerated neural network misc Noise-resistant misc Dynamic matrix pseudoinverse A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application |
| authorStr |
Li, Xiaoxiao |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)198692617 |
| format |
Article |
| dewey-ones |
000 - Computer science, information & general works |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1370-4621 |
| topic_title |
000 VZ A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application Zhang neural network Varying-parameter convergence-accelerated neural network Noise-resistant Dynamic matrix pseudoinverse |
| topic |
ddc 000 misc Zhang neural network misc Varying-parameter convergence-accelerated neural network misc Noise-resistant misc Dynamic matrix pseudoinverse |
| topic_unstemmed |
ddc 000 misc Zhang neural network misc Varying-parameter convergence-accelerated neural network misc Noise-resistant misc Dynamic matrix pseudoinverse |
| topic_browse |
ddc 000 misc Zhang neural network misc Varying-parameter convergence-accelerated neural network misc Noise-resistant misc Dynamic matrix pseudoinverse |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Neural processing letters |
| hierarchy_parent_id |
198692617 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Neural processing letters |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 |
| title |
A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application |
| ctrlnum |
(DE-627)OLC2124898833 (DE-He213)s11063-021-10440-x-p |
| title_full |
A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application |
| author_sort |
Li, Xiaoxiao |
| journal |
Neural processing letters |
| journalStr |
Neural processing letters |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
txt |
| container_start_page |
1287 |
| author_browse |
Li, Xiaoxiao Li, Shuai Xu, Zhihao Zhou, Xuefeng |
| container_volume |
53 |
| class |
000 VZ |
| format_se |
Aufsätze |
| author-letter |
Li, Xiaoxiao |
| doi_str_mv |
10.1007/s11063-021-10440-x |
| normlink |
(ORCID)0000-0003-1642-2059 |
| normlink_prefix_str_mv |
(orcid)0000-0003-1642-2059 |
| dewey-full |
000 |
| title_sort |
a vary-parameter convergence-accelerated recurrent neural network for online solving dynamic matrix pseudoinverse and its robot application |
| title_auth |
A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application |
| abstract |
Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstractGer |
Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT |
| container_issue |
2 |
| title_short |
A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application |
| url |
https://doi.org/10.1007/s11063-021-10440-x |
| remote_bool |
false |
| author2 |
Li, Shuai Xu, Zhihao Zhou, Xuefeng |
| author2Str |
Li, Shuai Xu, Zhihao Zhou, Xuefeng |
| ppnlink |
198692617 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11063-021-10440-x |
| up_date |
2024-07-04T01:48:38.452Z |
| _version_ |
1803611242194010112 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2124898833</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505093626.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-021-10440-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2124898833</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-021-10440-x-p</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Xiaoxiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Vary-Parameter Convergence-Accelerated Recurrent Neural Network for Online Solving Dynamic Matrix Pseudoinverse and its Robot Application</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Among this study, a vary-parameter convergence-accelerated neural network (VPCANN) model is generalized to solving dynamic matrix pseudoinverse, which can achieve super exponential convergence and noise-resistant, compared to the traditional Zhang neural network (ZNN) designed for dynamic problems. Simulative experiments reveal that the neural state solutions synthesized by the VPCANN can quickly approach to the theoretical pseudoinverse. Moreover, based on three types of noise disturbance including constant noise, random noise and dynamic noise, comparisons between the VPCANN and ZNN model are also investigated, verifying noise-resistant of the VPCANN model is better than the ZNN. In addition, to show the potential application of the VPCANN in practice, the kinematic motion planning of a six-links robot manipulator is considered, further substantiating the efficacy of the VPCANN in the dynamic matrix pseudoinverse.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Zhang neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Varying-parameter convergence-accelerated neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Noise-resistant</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic matrix pseudoinverse</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Shuai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Zhihao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Xuefeng</subfield><subfield code="0">(orcid)0000-0003-1642-2059</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural processing letters</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">53(2021), 2 vom: 19. Feb., Seite 1287-1304</subfield><subfield code="w">(DE-627)198692617</subfield><subfield code="w">(DE-600)1316823-X</subfield><subfield code="w">(DE-576)052842762</subfield><subfield code="x">1370-4621</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:53</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:2</subfield><subfield code="g">day:19</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:1287-1304</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11063-021-10440-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PSY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">53</subfield><subfield code="j">2021</subfield><subfield code="e">2</subfield><subfield code="b">19</subfield><subfield code="c">02</subfield><subfield code="h">1287-1304</subfield></datafield></record></collection>
|
| score |
7.3998547 |