Parameter identification of wiener models by multi-innovation algorithms
Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yao, Jian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Control, Automation and Systems - Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009, 11(2013), 6 vom: 29. Nov., Seite 1170-1176 |
|---|---|
| Übergeordnetes Werk: |
volume:11 ; year:2013 ; number:6 ; day:29 ; month:11 ; pages:1170-1176 |
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| DOI / URN: |
10.1007/s12555-012-0261-4 |
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SPR026373467 |
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| 520 | |a Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. | ||
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| 650 | 4 | |a parameter estimation |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Ji, Zhicheng |4 aut | |
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10.1007/s12555-012-0261-4 doi (DE-627)SPR026373467 (SPR)s12555-012-0261-4-e DE-627 ger DE-627 rakwb eng Yao, Jian verfasserin aut Parameter identification of wiener models by multi-innovation algorithms 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. Multi-innovation (dpeaa)DE-He213 parameter estimation (dpeaa)DE-He213 stochastic gradient methods (dpeaa)DE-He213 Wiener nonlinear system (dpeaa)DE-He213 Huang, Yanping aut Ji, Zhicheng aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 11(2013), 6 vom: 29. Nov., Seite 1170-1176 (DE-627)SPR026303256 nnns volume:11 year:2013 number:6 day:29 month:11 pages:1170-1176 https://dx.doi.org/10.1007/s12555-012-0261-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 11 2013 6 29 11 1170-1176 |
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10.1007/s12555-012-0261-4 doi (DE-627)SPR026373467 (SPR)s12555-012-0261-4-e DE-627 ger DE-627 rakwb eng Yao, Jian verfasserin aut Parameter identification of wiener models by multi-innovation algorithms 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. Multi-innovation (dpeaa)DE-He213 parameter estimation (dpeaa)DE-He213 stochastic gradient methods (dpeaa)DE-He213 Wiener nonlinear system (dpeaa)DE-He213 Huang, Yanping aut Ji, Zhicheng aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 11(2013), 6 vom: 29. Nov., Seite 1170-1176 (DE-627)SPR026303256 nnns volume:11 year:2013 number:6 day:29 month:11 pages:1170-1176 https://dx.doi.org/10.1007/s12555-012-0261-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 11 2013 6 29 11 1170-1176 |
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10.1007/s12555-012-0261-4 doi (DE-627)SPR026373467 (SPR)s12555-012-0261-4-e DE-627 ger DE-627 rakwb eng Yao, Jian verfasserin aut Parameter identification of wiener models by multi-innovation algorithms 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013 Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. Multi-innovation (dpeaa)DE-He213 parameter estimation (dpeaa)DE-He213 stochastic gradient methods (dpeaa)DE-He213 Wiener nonlinear system (dpeaa)DE-He213 Huang, Yanping aut Ji, Zhicheng aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 11(2013), 6 vom: 29. Nov., Seite 1170-1176 (DE-627)SPR026303256 nnns volume:11 year:2013 number:6 day:29 month:11 pages:1170-1176 https://dx.doi.org/10.1007/s12555-012-0261-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 11 2013 6 29 11 1170-1176 |
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Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
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Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
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Abstract An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013 |
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Parameter identification of wiener models by multi-innovation algorithms |
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Huang, Yanping Ji, Zhicheng |
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