Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems

Lijuan Wan, Ximei Liu, Feng Ding and Chunping Chen
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Lijuan Wan: College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Ximei Liu: College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Feng Ding: School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Chunping Chen: Editorial Office of Journal of Qingdao University of Science and Technology (Natural Science Edition), Qingdao University of Science and Technology, Qingdao 266061, China

Mathematics, 2019, vol. 7, issue 7, 1-20

Abstract: This paper is concerned with the identification problem for multivariable equation-error systems whose disturbance is an autoregressive moving average process. By means of the hierarchical identification principle and the iterative search, a hierarchical least-squares-based iterative (HLSI) identification algorithm is derived and a least-squares-based iterative (LSI) identification algorithm is given for comparison. Furthermore, a hierarchical multi-innovation least-squares-based iterative (HMILSI) identification algorithm is proposed using the multi-innovation theory. Compared with the LSI algorithm, the HLSI algorithm has smaller computational burden and can give more accurate parameter estimates and the HMILSI algorithm can track time-varying parameters. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.

Keywords: least-squares; iterative identification; hierarchical; parameter estimation; multivariable system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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