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
Hlavní autori: Mao, Yawen, Ding, Feng, Alsaedi, Ahmed, Hayat, Tasawar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.11.2016
Predmet:
Adaptive filtering
Adaptive filters
Algorithms
Computational efficiency
Convergence
Dynamical systems
Linear filters
Multi-innovation identification theory
Nonlinear dynamics
Nonlinear system
Parameter estimation
Recursive identification
Adaptive filtering
Recursive identification
Parameter estimation
Nonlinear system
Multi-innovation identification theory
ISSN:0165-1684, 1872-7557
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2016.05.009