Filtering-based parameter identification methods for multivariable stochastic systems

Huafeng Xia*, Feiyan Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Article number2254
Pages (from-to)1-19
Number of pages19
Issue number12
Publication statusPublished - Dec 2020


  • Adaptive filtering
  • Maximum likelihood
  • Multi-innovation identification theory
  • Multivariable system


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