Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique

Qinyao Liu, Feng Ding*, Ling Xu, Erfu Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


System identification provides many convenient and useful methods for engineering modelling. This study targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the coloured noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then the filtered system is decomposed into several subsystems and a filtering-based partially-coupled generalised extended stochastic gradient algorithm is developed via the coupling concept. In contrast to the multivariable generalised extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples.

Original languageEnglish
Pages (from-to)42-650
Number of pages609
JournalIET Control Theory and Applications
Issue number5
Publication statusPublished - 2019
Externally publishedYes

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