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Recursive least squares estimation methods for a class of nonlinear systems based on non‐uniform sampling
Author(s) -
Liu Qilin,
Chen Feiyan,
Ding Feng,
Alsaedi Ahmed,
Hayat Tasawar
Publication year - 2021
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3263
Subject(s) - nonlinear system , sampling (signal processing) , class (philosophy) , system identification , identification (biology) , estimation theory , least squares function approximation , computer science , non linear least squares , mathematical optimization , nonlinear system identification , algorithm , recursive least squares filter , mathematics , artificial intelligence , data modeling , statistics , adaptive filter , filter (signal processing) , quantum mechanics , database , estimator , computer vision , biology , physics , botany
Summary Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein‐Wiener nonlinear systems based on non‐uniform sampling. By means of the auxiliary model identification idea, an auxiliary model‐based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model‐based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.