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Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise
Author(s) -
Mao Yawen,
Ding Feng,
Yang Erfu
Publication year - 2017
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.2772
Subject(s) - estimator , autoregressive model , noise (video) , adaptive filter , algorithm , filter (signal processing) , convergence (economics) , recursive least squares filter , nonlinear system , computer science , estimation theory , identification (biology) , control theory (sociology) , mathematics , artificial intelligence , statistics , image (mathematics) , quantum mechanics , physics , botany , control (management) , economics , computer vision , biology , economic growth
Summary In this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.