Maximum Likelihood-Based Recursive Least-Squares Algorithm for Multivariable Systems with Colored Noises Using the Decomposition Technique

被引：0

作者：

Huafeng Xia

Yan Ji

Ling Xu

Tasawar Hayat

机构：

[1] Jiangnan University,Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)

[2] Taizhou University,Taizhou Electric Power Conversion and Control Engineering Technology Research Center

[3] Qingdao University of Science and Technology,College of Automation and Electronic Engineering

[4] King Abdulaziz University,Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science

来源：

Circuits, Systems, and Signal Processing | 2019年 / 38卷

关键词：

Parameter estimation; Maximum likelihood; Decomposition technique; Least-squares; Multivariable system;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper considers the parameter estimation problems for a class of multivariable equation-error systems with colored noises. By using the decomposition technique, a multivariable system is transformed into several subsystems to reduce the computational burden, and a maximum likelihood-based recursive least-squares identification algorithm is developed for estimating the parameters of each subsystem. As a comparison, a multivariable recursive extended least-squares algorithm is presented. The analysis indicates that the proposed algorithm has lower computational complexity than the multivariable recursive extended least-squares algorithm, and the numerical simulation results demonstrate that the proposed method is effective.

引用

页码：986 / 1004

页数：18

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