Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems
Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Ching-Hung [verfasserIn] Wang, Bo-Hang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 13(2008), 1 vom: 07. März, Seite 1-12 |
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| Übergeordnetes Werk: |
volume:13 ; year:2008 ; number:1 ; day:07 ; month:03 ; pages:1-12 |
| Links: |
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| DOI / URN: |
10.1007/s00500-008-0287-y |
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| 520 | |a Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. | ||
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10.1007/s00500-008-0287-y doi (DE-627)SPR00647537X (SPR)s00500-008-0287-y-e DE-627 ger DE-627 rakwb eng Lee, Ching-Hung verfasserin aut Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. Adaptive control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 CMAC (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Wang, Bo-Hang verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 13(2008), 1 vom: 07. März, Seite 1-12 (DE-627)SPR006469531 nnns volume:13 year:2008 number:1 day:07 month:03 pages:1-12 https://dx.doi.org/10.1007/s00500-008-0287-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2008 1 07 03 1-12 |
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10.1007/s00500-008-0287-y doi (DE-627)SPR00647537X (SPR)s00500-008-0287-y-e DE-627 ger DE-627 rakwb eng Lee, Ching-Hung verfasserin aut Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. Adaptive control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 CMAC (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Wang, Bo-Hang verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 13(2008), 1 vom: 07. März, Seite 1-12 (DE-627)SPR006469531 nnns volume:13 year:2008 number:1 day:07 month:03 pages:1-12 https://dx.doi.org/10.1007/s00500-008-0287-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2008 1 07 03 1-12 |
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10.1007/s00500-008-0287-y doi (DE-627)SPR00647537X (SPR)s00500-008-0287-y-e DE-627 ger DE-627 rakwb eng Lee, Ching-Hung verfasserin aut Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. Adaptive control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 CMAC (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Wang, Bo-Hang verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 13(2008), 1 vom: 07. März, Seite 1-12 (DE-627)SPR006469531 nnns volume:13 year:2008 number:1 day:07 month:03 pages:1-12 https://dx.doi.org/10.1007/s00500-008-0287-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2008 1 07 03 1-12 |
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10.1007/s00500-008-0287-y doi (DE-627)SPR00647537X (SPR)s00500-008-0287-y-e DE-627 ger DE-627 rakwb eng Lee, Ching-Hung verfasserin aut Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. Adaptive control (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 CMAC (dpeaa)DE-He213 Wavelet (dpeaa)DE-He213 Wang, Bo-Hang verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 13(2008), 1 vom: 07. März, Seite 1-12 (DE-627)SPR006469531 nnns volume:13 year:2008 number:1 day:07 month:03 pages:1-12 https://dx.doi.org/10.1007/s00500-008-0287-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2008 1 07 03 1-12 |
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Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems |
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Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. |
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Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. |
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Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems. |
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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">SPR00647537X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002722.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2008 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-008-0287-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR00647537X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-008-0287-y-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">Lee, Ching-Hung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</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="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive control</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CMAC</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wavelet</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Bo-Hang</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">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">13(2008), 1 vom: 07. März, Seite 1-12</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2008</subfield><subfield code="g">number:1</subfield><subfield code="g">day:07</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:1-12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-008-0287-y</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_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2008</subfield><subfield code="e">1</subfield><subfield code="b">07</subfield><subfield code="c">03</subfield><subfield code="h">1-12</subfield></datafield></record></collection>
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