Decomposition based recursive least squares parameter estimation for input nonlinear equation-error systems
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- 英文论文题名：Decomposition based recursive least squares parameter estimation for input nonlinear equation-error systems
- 论文作者：CHEN Huibo ; FAN Jiangbo
- 英文论文作者：CHEN Huibo ; FAN Jiangbo ; School of Electrical Engineering ; Sanmenxia Polytechnic ; Department of Automatic Control ; Henan Institute of Technology
- 年：2017
- 作者机构：School of Electrical Engineering,Sanmenxia Polytechnic;Department of Automatic Control,Henan Institute of Technology;
- 英文论文关键词：Least squares ; model decomposition ; parameter estimation
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：O241.5
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：250k
- 原文格式：D
- 会议级别：全国
摘要
This paper developed a decomposition based recursive least square algorithm for estimating the parameters of an input nonlinear equation-error system, where the nonlinear system was parameterized as a bilinear-parameter system and decomposed it into two subsystems whose parameters were cross-estimated by the least squares methods. The proposed algorithm estimated much less parameters than the over-parameterization identification methods. Simulation results confirm the effectiveness of the proposed algorithm.
This paper developed a decomposition based recursive least square algorithm for estimating the parameters of an input nonlinear equation-error system, where the nonlinear system was parameterized as a bilinear-parameter system and decomposed it into two subsystems whose parameters were cross-estimated by the least squares methods. The proposed algorithm estimated much less parameters than the over-parameterization identification methods. Simulation results confirm the effectiveness of the proposed algorithm.
This paper developed a decomposition based recursive least square algorithm for estimating the parameters of an input nonlinear equation-error system, where the nonlinear system was parameterized as a bilinear-parameter system and decomposed it into two subsystems whose parameters were cross-estimated by the least squares methods. The proposed algorithm estimated much less parameters than the over-parameterization identification methods. Simulation results confirm the effectiveness of the proposed algorithm.
引文
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[2]S.J.Nanda,G.Panda,B.Majhi,Improved identification of Hammerstein plants using new CPSO and IPSO algorithms,Expert Systems with Applications,Vol.37,No.10,6818-6831,2010
[3]F.Ding,System Identification—New Theory and Methods,Science Press,Beijing,2013.
[4]W.X.Zhao,H.F.Chen,Identification of Wiener,Hammerstein,and NARX systems as Markov chains with improved estimates for their nonlinearities,System Control Letter,Vol.61,No.12,1175-1186,2012
[5]A.Hagenblada,L.Ljung,A.Wills,Maximum likelihood identification of Wiener models,Automatica,Vol.44,No.11,2697-2705,2008
[6]A.Wills,T.B.Sch?n,L.Ljung,and B.Ninness,Identification of Hammerstein-Wiener models,Automatica,Vol.49,No.1,70-81,2013
[7]L.Y.Wang,W.X.Zhao,System identification:new Paradigms,challenges,and opportunities,Acta Automatica Sinica,Vol.39,No.7,933-942,2013
[8]S.I.Biagiola and J.L.Figueroa,Wiener and Hammerstein uncertain models identification,Mathematics and Computers in Simulation,Vol.79,No.11,3296-3313,2009
[9]F.Ding,Complexity,convergence and computational efficiency for system identification algorithms,Control and Decision,Vol.31,No.10,1729-1741,2016
[10]J.Kim,K.Konstantinou,Digital predistortion of wide band signals based on power amplifier model with memory,Electronic Letter,Vol.37,No.23,1417-1418,2001
[11]S.W.Su,L.Wang and ect.,Identification and control for heart rate regulation during treadmill exercise,IEEE transactions on bio-medical engineering,Vol.54,No.7,1238-1246,2007
[12]X.H.Wang,F.Ding,Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique,Circuits,Systems and Signal Processing Vol.35,No.2,481-500,2016
[13]E.W.Bai,Decoupling the linear and nonlinear parts in Hammerstein model identification,Automatica,Vol.40,No.4,671-676,2004
[14]J.V?r?s,Parameter identification of discontinuous Hammerstein systems,Automatica,Vol.33,No.6,1141-1146 1997
[15]C.Wang,T.Tang,Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems,Applied Mathematics Letters,Vol.29,36-41,2014
[16]E.W.Bai,A blind approach to the Hammerstein-Wiener model identification,Automatica,Vol.38,No.6,967-979,2002
[17]J.V?r?s,Iterative algorithm for parameter identification of Hammerstein systems with two-segment nonlinearities,IEEE Transactions on Automatic Control Control,Vol.44,No.11,2145-2149,1999
[18]J.V?r?s,Modeling and parameter identification of systems with multi-segment piecewise-linear characteristics,IEEE Transactions on Automatic Control,Vol.47,No.1,184-188,2002
[19]F.Ding,X.P.Liu,G.Liu,Identification methods for Hammerstein nonlinear systems,Digital Signal Processing,Vol.21,No.2,215-238,2011
[20]J.H.Li,Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration,Applied Mathematics Letters,Vol.26,No.1,91-96,2013
[21]E.W.Bai,K.Li,Convergence of the iterative algorithm for a general Hammerstein system identification,Automatica,Vol.46,No.11,1891-1896,2010
[22]X.H.Wang,F.Ding,Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems.International Journal of Adaptive Control and Signal Processing,Vol.30,No.6,824-842,2016
[23]F.Ding,F.F.Wang,Recursive least squares identification algorithms for linear-in-parameter systems with missing data,Control and Decision,Vol.31,No.12,2261-2266,2016
[24]F.Ding,System Identification—Multi-Innovation Identification Theory and Methods,Science Press,Beijing,2016
[25]X.H.Wang,F.Ding,Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle,Signal Processing,Vol.117,208-218,2015
[26]H.B.Chen,F.Ding,Hierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systems,Circuit,System and Signal Processing,Vol.34,No.1,61-75,2015
[27]H.B.Chen,Y.S Xiao,F.Ding,Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle,Applied Mathematics and Computation,Vol.247,1202-1210,2014
[28]H.B.Chen,F.Ding,Y.S Xiao,Decomposition based parameter estimation algorithm for input nonlinear systems using the key term separation technique,Nonlinear Dynamics,Vol.79,No.3,2027-2035,2015
[29]F.Ding,System Identification—Performances Analysis for Identification Methods,Science Press,Beijing,2014
[30]F.Ding,System Identification—Auxiliary Model Identification Idea and Methods,Science Press,Beijing,2017
[31]X.H.Wang,F.Ding,F.E.Alsaadi,T.Hayat,Convergence analysis of the hierarchical least squares algorithm for bilinearin-parameter systems,Circuit,System and Signal Processing,Vol.35,No.12,4307-4330,2016
[2]S.J.Nanda,G.Panda,B.Majhi,Improved identification of Hammerstein plants using new CPSO and IPSO algorithms,Expert Systems with Applications,Vol.37,No.10,6818-6831,2010
[3]F.Ding,System Identification—New Theory and Methods,Science Press,Beijing,2013.
[4]W.X.Zhao,H.F.Chen,Identification of Wiener,Hammerstein,and NARX systems as Markov chains with improved estimates for their nonlinearities,System Control Letter,Vol.61,No.12,1175-1186,2012
[5]A.Hagenblada,L.Ljung,A.Wills,Maximum likelihood identification of Wiener models,Automatica,Vol.44,No.11,2697-2705,2008
[6]A.Wills,T.B.Sch?n,L.Ljung,and B.Ninness,Identification of Hammerstein-Wiener models,Automatica,Vol.49,No.1,70-81,2013
[7]L.Y.Wang,W.X.Zhao,System identification:new Paradigms,challenges,and opportunities,Acta Automatica Sinica,Vol.39,No.7,933-942,2013
[8]S.I.Biagiola and J.L.Figueroa,Wiener and Hammerstein uncertain models identification,Mathematics and Computers in Simulation,Vol.79,No.11,3296-3313,2009
[9]F.Ding,Complexity,convergence and computational efficiency for system identification algorithms,Control and Decision,Vol.31,No.10,1729-1741,2016
[10]J.Kim,K.Konstantinou,Digital predistortion of wide band signals based on power amplifier model with memory,Electronic Letter,Vol.37,No.23,1417-1418,2001
[11]S.W.Su,L.Wang and ect.,Identification and control for heart rate regulation during treadmill exercise,IEEE transactions on bio-medical engineering,Vol.54,No.7,1238-1246,2007
[12]X.H.Wang,F.Ding,Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique,Circuits,Systems and Signal Processing Vol.35,No.2,481-500,2016
[13]E.W.Bai,Decoupling the linear and nonlinear parts in Hammerstein model identification,Automatica,Vol.40,No.4,671-676,2004
[14]J.V?r?s,Parameter identification of discontinuous Hammerstein systems,Automatica,Vol.33,No.6,1141-1146 1997
[15]C.Wang,T.Tang,Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems,Applied Mathematics Letters,Vol.29,36-41,2014
[16]E.W.Bai,A blind approach to the Hammerstein-Wiener model identification,Automatica,Vol.38,No.6,967-979,2002
[17]J.V?r?s,Iterative algorithm for parameter identification of Hammerstein systems with two-segment nonlinearities,IEEE Transactions on Automatic Control Control,Vol.44,No.11,2145-2149,1999
[18]J.V?r?s,Modeling and parameter identification of systems with multi-segment piecewise-linear characteristics,IEEE Transactions on Automatic Control,Vol.47,No.1,184-188,2002
[19]F.Ding,X.P.Liu,G.Liu,Identification methods for Hammerstein nonlinear systems,Digital Signal Processing,Vol.21,No.2,215-238,2011
[20]J.H.Li,Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration,Applied Mathematics Letters,Vol.26,No.1,91-96,2013
[21]E.W.Bai,K.Li,Convergence of the iterative algorithm for a general Hammerstein system identification,Automatica,Vol.46,No.11,1891-1896,2010
[22]X.H.Wang,F.Ding,Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems.International Journal of Adaptive Control and Signal Processing,Vol.30,No.6,824-842,2016
[23]F.Ding,F.F.Wang,Recursive least squares identification algorithms for linear-in-parameter systems with missing data,Control and Decision,Vol.31,No.12,2261-2266,2016
[24]F.Ding,System Identification—Multi-Innovation Identification Theory and Methods,Science Press,Beijing,2016
[25]X.H.Wang,F.Ding,Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle,Signal Processing,Vol.117,208-218,2015
[26]H.B.Chen,F.Ding,Hierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systems,Circuit,System and Signal Processing,Vol.34,No.1,61-75,2015
[27]H.B.Chen,Y.S Xiao,F.Ding,Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle,Applied Mathematics and Computation,Vol.247,1202-1210,2014
[28]H.B.Chen,F.Ding,Y.S Xiao,Decomposition based parameter estimation algorithm for input nonlinear systems using the key term separation technique,Nonlinear Dynamics,Vol.79,No.3,2027-2035,2015
[29]F.Ding,System Identification—Performances Analysis for Identification Methods,Science Press,Beijing,2014
[30]F.Ding,System Identification—Auxiliary Model Identification Idea and Methods,Science Press,Beijing,2017
[31]X.H.Wang,F.Ding,F.E.Alsaadi,T.Hayat,Convergence analysis of the hierarchical least squares algorithm for bilinearin-parameter systems,Circuit,System and Signal Processing,Vol.35,No.12,4307-4330,2016