Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems

This paper presents an adaptive filtering-based maximum likelihood multi-innovation extended stochastic gradient algorithm to identify multivariable equation-error systems with colored noises. The data filtering and model decomposition techniques are used to simplify the structure of the considered...

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Bibliographic Details
Published in:Mathematics (Basel) 2020-12, Vol.8 (12), p.2254
Main Authors: Xia, Huafeng, Chen, Feiyan
Format: Article
Language:English
Subjects:
adaptive filtering
maximum likelihood
multi-innovation identification theory
multivariable system
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Summary:This paper presents an adaptive filtering-based maximum likelihood multi-innovation extended stochastic gradient algorithm to identify multivariable equation-error systems with colored noises. The data filtering and model decomposition techniques are used to simplify the structure of the considered system, in which a predefined filter is utilized to filter the observed data, and the multivariable system is turned into several subsystems whose parameters appear in the vectors. By introducing the multi-innovation identification theory to the stochastic gradient method, this study produces improved performances. The simulation numerical results indicate that the proposed algorithm can generate more accurate parameter estimates than the filtering-based maximum likelihood recursive extended stochastic gradient algorithm.
ISSN:2227-7390
2227-7390
DOI:10.3390/math8122254