Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model |
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| Authors: | Qinyao Liu Feng Ding Ahmed Alsaedi Tasawar Hayat |
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| Affiliation: | 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering,Jiangnan University,Wuxi,P. R. China;2.NAAM Research Group, Department of Mathematics, Faculty of Science,King Abdulaziz University,Jeddah,Saudi Arabia;3.Department of Mathematics,Quaid-I-Azam University,Islamabad,Pakistan |
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| Abstract: | 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. |
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