Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering |
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Authors: | Ping Ma 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,China;2.Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics,King Abdulaziz University,Jeddah,Saudi Arabia;3.Department of Mathematics,Quaid-I-Azam University,Islamabad,Pakistan |
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Abstract: | This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective. |
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