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Bias compensation‐based parameter estimation for output error moving average systems
Author(s) -
Ding Jie,
Ding Feng
Publication year - 2011
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.1266
Subject(s) - compensation (psychology) , noise (video) , term (time) , variance (accounting) , estimation theory , identification (biology) , control theory (sociology) , unbiased estimation , computer science , statistics , colors of noise , least squares function approximation , mathematics , artificial intelligence , white noise , botany , accounting , physics , quantum mechanics , estimator , psychoanalysis , business , image (mathematics) , biology , psychology , control (management)
SUMMARY Identification problems of output error models with moving average noises are considered in this paper. The least‐squares‐based parameter estimation is biased under the colored noises in outputs. Firstly, a bias compensation term is formulated to achieve the bias‐eliminated estimates of the system parameters. Secondly, the bias compensation term is determined by the unknown variance of the noise and the unknown noise model, thus based on the hierarchical identification principle, an unbiased parameter estimation is obtained by interactively estimating noise variance and noise parameters. Finally, the estimated bias compensation term is added to the biased parameter estimates. The simulation examples confirm the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.