Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model
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This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.
KeywordsAuxiliary model multivariate system parameter estimation recursive identification
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