Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
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DOI：
10.1016/j.camwa.2008.07.015
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发表时间：
2008-12
期刊：
影响因子：
--
通讯作者：
Dongqing Wang;F. Ding
中科院分区：
文献类型：
--
作者：
Dongqing Wang;F. Ding
An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein–Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. Two methods of separating the parameter estimates of the original parameters from the product terms are discussed: the average method and the singular value decomposition method. To improve the identification accuracy, an extended stochastic gradient algorithm with a forgetting factor is presented. The simulation results indicate that the parameter estimation errors become small by introducing the forgetting factor.
