Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system
Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance o...
Ausführliche Beschreibung
Autor*in: |
Ting, Jung-Chu [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Anmerkung: |
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Übergeordnetes Werk: |
Enthalten in: Journal of mechanical science and technology - Berlin : Springer, 2005, 32(2018), 9 vom: Sept., Seite 4399-4412 |
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Übergeordnetes Werk: |
volume:32 ; year:2018 ; number:9 ; month:09 ; pages:4399-4412 |
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DOI / URN: |
10.1007/s12206-018-0838-9 |
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Katalog-ID: |
SPR025340255 |
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520 | |a Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. | ||
650 | 4 | |a Synchronous reluctance motor |7 (dpeaa)DE-He213 | |
650 | 4 | |a Recurrent Hermite polynomial neural network |7 (dpeaa)DE-He213 | |
650 | 4 | |a Lyapunov stability |7 (dpeaa)DE-He213 | |
650 | 4 | |a Continuously variable transmission |7 (dpeaa)DE-He213 | |
700 | 1 | |a Chen, Der-Fa |4 aut | |
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10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 9 09 4399-4412 |
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10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 9 09 4399-4412 |
allfields_unstemmed |
10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 9 09 4399-4412 |
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10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 9 09 4399-4412 |
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10.1007/s12206-018-0838-9 doi (DE-627)SPR025340255 (SPR)s12206-018-0838-9-e DE-627 ger DE-627 rakwb eng Ting, Jung-Chu verfasserin aut Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 Chen, Der-Fa aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 32(2018), 9 vom: Sept., Seite 4399-4412 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:32 year:2018 number:9 month:09 pages:4399-4412 https://dx.doi.org/10.1007/s12206-018-0838-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2018 9 09 4399-4412 |
language |
English |
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Enthalten in Journal of mechanical science and technology 32(2018), 9 vom: Sept., Seite 4399-4412 volume:32 year:2018 number:9 month:09 pages:4399-4412 |
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Enthalten in Journal of mechanical science and technology 32(2018), 9 vom: Sept., Seite 4399-4412 volume:32 year:2018 number:9 month:09 pages:4399-4412 |
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topic_facet |
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Ting, Jung-Chu @@aut@@ Chen, Der-Fa @@aut@@ |
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Ting, Jung-Chu |
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Ting, Jung-Chu misc Synchronous reluctance motor misc Recurrent Hermite polynomial neural network misc Lyapunov stability misc Continuously variable transmission Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system |
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Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system Synchronous reluctance motor (dpeaa)DE-He213 Recurrent Hermite polynomial neural network (dpeaa)DE-He213 Lyapunov stability (dpeaa)DE-He213 Continuously variable transmission (dpeaa)DE-He213 |
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Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system |
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novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system |
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Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system |
abstract |
Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
abstractGer |
Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
abstract_unstemmed |
Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR025340255</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230403065709.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12206-018-0838-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR025340255</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12206-018-0838-9-e</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="100" ind1="1" ind2=" "><subfield code="a">Ting, Jung-Chu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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="500" ind1=" " ind2=" "><subfield code="a">© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Synchronous reluctance motor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recurrent Hermite polynomial neural network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Lyapunov stability</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Continuously variable transmission</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Der-Fa</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of mechanical science and technology</subfield><subfield code="d">Berlin : Springer, 2005</subfield><subfield code="g">32(2018), 9 vom: Sept., Seite 4399-4412</subfield><subfield code="w">(DE-627)58714016X</subfield><subfield code="w">(DE-600)2467571-4</subfield><subfield code="x">1976-3824</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:9</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:4399-4412</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s12206-018-0838-9</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=" 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