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• W4292262838 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." @default.
• W4292262838 created "2022-08-19" @default.
• W4292262838 creator A5043878756 @default.
• W4292262838 creator A5073216396 @default.
• W4292262838 date "2022-08-16" @default.
• W4292262838 modified "2023-10-03" @default.
• W4292262838 title "Partially Coupled Stochastic Gradient Estimation for Multivariate Equation-Error Systems" @default.
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• W4292262838 doi "https://doi.org/10.3390/math10162955" @default.
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