Adaptive filtering parameter estimation algorithms for Hammerstein nonlinear systems
This paper studies the parameter estimation problems of the Hammerstein nonlinear systems using the adaptive filtering technique. A linear filter based recursive least squares (LF-RLS) identification algorithm with good convergence properties and high parameter estimation accuracy is proposed by fil...
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| Vydané v: | Signal processing Ročník 128; s. 417 - 425 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.11.2016
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| ISSN: | 0165-1684, 1872-7557 |
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| Abstract | This paper studies the parameter estimation problems of the Hammerstein nonlinear systems using the adaptive filtering technique. A linear filter based recursive least squares (LF-RLS) identification algorithm with good convergence properties and high parameter estimation accuracy is proposed by filtering the input-output data. A linear filter based multi-innovation stochastic gradient (LF-MISG) algorithm is proposed by the innovation expansion, in order to improve the computational efficiency of the LF-RLS algorithm. Furthermore, a time-varying factor is introduced in the linear filter to improve the convergence speed of the LF-MISG algorithm. The efficiency of the proposed algorithms are shown in comparison with the conventional identification algorithms.
•Two filtering based identification methods are discussed for Hammerstein systems.•A filter based recursive least squares method is presented for Hammerstein systems.•A filter based multi-innovation stochastic gradient method is given for comparison. |
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| AbstractList | This paper studies the parameter estimation problems of the Hammerstein nonlinear systems using the adaptive filtering technique. A linear filter based recursive least squares (LF-RLS) identification algorithm with good convergence properties and high parameter estimation accuracy is proposed by filtering the input-output data. A linear filter based multi-innovation stochastic gradient (LF-MISG) algorithm is proposed by the innovation expansion, in order to improve the computational efficiency of the LF-RLS algorithm. Furthermore, a time-varying factor is introduced in the linear filter to improve the convergence speed of the LF-MISG algorithm. The efficiency of the proposed algorithms are shown in comparison with the conventional identification algorithms.
•Two filtering based identification methods are discussed for Hammerstein systems.•A filter based recursive least squares method is presented for Hammerstein systems.•A filter based multi-innovation stochastic gradient method is given for comparison. This paper studies the parameter estimation problems of the Hammerstein nonlinear systems using the adaptive filtering technique. A linear filter based recursive least squares (LF-RLS) identification algorithm with good convergence properties and high parameter estimation accuracy is proposed by filtering the input-output data. A linear filter based multi-innovation stochastic gradient (LF-MISG) algorithm is proposed by the innovation expansion, in order to improve the computational efficiency of the LF-RLS algorithm. Furthermore, a time-varying factor is introduced in the linear filter to improve the convergence speed of the LF-MISG algorithm. The efficiency of the proposed algorithms are shown in comparison with the conventional identification algorithms. |
| Author | Mao, Yawen Ding, Feng Alsaedi, Ahmed Hayat, Tasawar |
| Author_xml | – sequence: 1 givenname: Yawen surname: Mao fullname: Mao, Yawen organization: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China – sequence: 2 givenname: Feng surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China – sequence: 3 givenname: Ahmed surname: Alsaedi fullname: Alsaedi, Ahmed organization: Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia – sequence: 4 givenname: Tasawar surname: Hayat fullname: Hayat, Tasawar organization: Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| SubjectTerms | Adaptive filtering Adaptive filters Algorithms Computational efficiency Convergence Dynamical systems Linear filters Multi-innovation identification theory Nonlinear dynamics Nonlinear system Parameter estimation Recursive identification |
| Title | Adaptive filtering parameter estimation algorithms for Hammerstein nonlinear systems |
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