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Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems

Author

Listed:
  • Ping Ma

    (Jiangsu Key Laboratory of Media Design and Software Technology, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China)

  • Lei Wang

    (School of Automation, Wuxi University, Wuxi 214105, China)

Abstract

This paper researches the identification problem for the unknown parameters of the multivariate equation-error autoregressive systems. Firstly, the original identification model is decomposed into several sub-identification models according to the number of system outputs. Then, based on the characteristic that the information vector and the parameter vector are common among the sub-identification models, the coupling identification concept is used to propose a partially coupled generalized stochastic gradient algorithm. Furthermore, by expanding the scalar innovation of each subsystem model to the innovation vector, a partially coupled multi-innovation generalized stochastic gradient algorithm is proposed. Finally, the numerical simulations indicate that the proposed algorithms are effective and have good parameter estimation performances.

Suggested Citation

  • Ping Ma & Lei Wang, 2022. "Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2955-:d:889401
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    References listed on IDEAS

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    1. Upadhyay, Ranjit Kumar & Paul, Chinmoy & Mondal, Argha & Vishwakarma, Gajendra K., 2018. "Estimation of biophysical parameters in a neuron model under random fluctuations," Applied Mathematics and Computation, Elsevier, vol. 329(C), pages 364-373.
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