Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise |
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| Authors: | Lijuan Liu Quanmin Zhu |
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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, People's Republic of China;2. Department of Engineering Design and Mathematics, University of the West of England, Bristol, England |
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| Abstract: | This paper considers the recursive identification problems for a class of multivariate autoregressive equation-error systems with autoregressive noise. By decomposing the system into several regressive identification subsystems, a maximum likelihood recursive generalised least squares identification algorithm is proposed to identify the parameter vectors in each subsystem. In addition, a multivariate recursive generalised least squares algorithm is derived as a comparison. The numerical simulation results indicate that the maximum likelihood recursive generalised least squares algorithm can effectively estimate the parameters of the multivariate autoregressive equation-error autoregressive systems and get more accurate parameter estimates than the multivariate recursive generalised least squares algorithm. |
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| Keywords: | Recursive identification multivariate system maximum likelihood recursive least squares |
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