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Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation |
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| Authors: | Ping Ma Quanmin Zhu |
| Affiliation: | 1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;2. Department of Engineering Design and Mathematics, University of the West of England, Bristol, England |
| Abstract: | This paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems. A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems. In order to further improve the parameter accuracy, a decomposition based multi-innovation recursive generalised least squares algorithm is developed by means of the multi-innovation theory. The simulation results confirm that these two algorithms are effective. |
| Keywords: | Parameter estimation least squares multi-innovation decomposition technique multivariate system |