Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control

This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is adde...
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Gespeichert in:

Autor*in:	Qi Pan [verfasserIn] Yanli Zhou [verfasserIn] Juntao Fei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	active power filter fuzzy neural network multi-feedback feature selection super-twisting sliding mode control

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 11(2023), 6, p 1495
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:6, p 1495

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math11061495

Katalog-ID:	DOAJ087300958

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spelling	10.3390/math11061495 doi (DE-627)DOAJ087300958 (DE-599)DOAJ7f748d1b67ce4051ba9d8b82e78463a6 DE-627 ger DE-627 rakwb eng QA1-939 Qi Pan verfasserin aut Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller. active power filter fuzzy neural network multi-feedback feature selection super-twisting sliding mode control Mathematics Yanli Zhou verfasserin aut Juntao Fei verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 6, p 1495 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:6, p 1495 https://doi.org/10.3390/math11061495 kostenfrei https://doaj.org/article/7f748d1b67ce4051ba9d8b82e78463a6 kostenfrei https://www.mdpi.com/2227-7390/11/6/1495 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 6, p 1495
allfields_unstemmed	10.3390/math11061495 doi (DE-627)DOAJ087300958 (DE-599)DOAJ7f748d1b67ce4051ba9d8b82e78463a6 DE-627 ger DE-627 rakwb eng QA1-939 Qi Pan verfasserin aut Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller. active power filter fuzzy neural network multi-feedback feature selection super-twisting sliding mode control Mathematics Yanli Zhou verfasserin aut Juntao Fei verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 6, p 1495 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:6, p 1495 https://doi.org/10.3390/math11061495 kostenfrei https://doaj.org/article/7f748d1b67ce4051ba9d8b82e78463a6 kostenfrei https://www.mdpi.com/2227-7390/11/6/1495 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 6, p 1495
allfieldsGer	10.3390/math11061495 doi (DE-627)DOAJ087300958 (DE-599)DOAJ7f748d1b67ce4051ba9d8b82e78463a6 DE-627 ger DE-627 rakwb eng QA1-939 Qi Pan verfasserin aut Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller. active power filter fuzzy neural network multi-feedback feature selection super-twisting sliding mode control Mathematics Yanli Zhou verfasserin aut Juntao Fei verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 6, p 1495 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:6, p 1495 https://doi.org/10.3390/math11061495 kostenfrei https://doaj.org/article/7f748d1b67ce4051ba9d8b82e78463a6 kostenfrei https://www.mdpi.com/2227-7390/11/6/1495 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 6, p 1495
allfieldsSound	10.3390/math11061495 doi (DE-627)DOAJ087300958 (DE-599)DOAJ7f748d1b67ce4051ba9d8b82e78463a6 DE-627 ger DE-627 rakwb eng QA1-939 Qi Pan verfasserin aut Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller. active power filter fuzzy neural network multi-feedback feature selection super-twisting sliding mode control Mathematics Yanli Zhou verfasserin aut Juntao Fei verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 6, p 1495 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:6, p 1495 https://doi.org/10.3390/math11061495 kostenfrei https://doaj.org/article/7f748d1b67ce4051ba9d8b82e78463a6 kostenfrei https://www.mdpi.com/2227-7390/11/6/1495 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 6, p 1495
language	English
source	In Mathematics 11(2023), 6, p 1495 volume:11 year:2023 number:6, p 1495
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topic_facet	active power filter fuzzy neural network multi-feedback feature selection super-twisting sliding mode control Mathematics
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authorswithroles_txt_mv	Qi Pan @@aut@@ Yanli Zhou @@aut@@ Juntao Fei @@aut@@
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callnumber-first	Q - Science
author	Qi Pan
spellingShingle	Qi Pan misc QA1-939 misc active power filter misc fuzzy neural network misc multi-feedback feature selection super-twisting sliding mode control misc Mathematics Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control
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topic_title	QA1-939 Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control active power filter fuzzy neural network multi-feedback feature selection super-twisting sliding mode control
topic	misc QA1-939 misc active power filter misc fuzzy neural network misc multi-feedback feature selection super-twisting sliding mode control misc Mathematics
topic_unstemmed	misc QA1-939 misc active power filter misc fuzzy neural network misc multi-feedback feature selection super-twisting sliding mode control misc Mathematics
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title	Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control
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title_sort	feature selection fuzzy neural network super-twisting harmonic control
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title_auth	Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control
abstract	This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller.
abstractGer	This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller.
abstract_unstemmed	This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller.
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title_short	Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control
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score	7.400526

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Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control

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