Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems

Huafeng Xia and Feiyan Chen
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Huafeng Xia: Taizhou Electric Power Conversion and Control Engineering Technology Research Center, Taizhou University, Taizhou 225300, China
Feiyan Chen: Department of Mathematical Sciences, Xi’an Jiaotong Liverpool University, Suzhou 215123, China

Mathematics, 2020, vol. 8, issue 12, 1-19

Abstract: 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.

Keywords: adaptive filtering; maximum likelihood; multi-innovation identification theory; multivariable system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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