Regular Papers
International Journal of Control, Automation and Systems 2023; 21(1): 151-160
Published online December 20, 2022
https://doi.org/10.1007/s12555-021-0923-1
© The International Journal of Control, Automation, and Systems
The Filtering Based Maximum Likelihood Recursive Least Squares Parameter Estimation Algorithms for a Class of Nonlinear Stochastic Systems with Colored Noise
This paper focuses on the maximum likelihood estimation for bilinear systems in the presence of colored noise. The state variables in the model is eliminated and an input-output expression is provided. The input-output data of the system is filtered by an estimated noise transfer function, and the system is transformed into two subsystems. A filtering based maximum likelihood recursive least squares algorithm is proposed to strengthen the identification accuracy and improve computational efficiency. The superior performance of the developed methods are demonstrated by numerical simulations.
Keywords Bilinear system, data filtering, least squares, maximum likelihood.
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Regular Papers
International Journal of Control, Automation and Systems 2023; 21(1): 151-160
Published online January 1, 2023 https://doi.org/10.1007/s12555-021-0923-1
Copyright © The International Journal of Control, Automation, and Systems.
The Filtering Based Maximum Likelihood Recursive Least Squares Parameter Estimation Algorithms for a Class of Nonlinear Stochastic Systems with Colored Noise
Longjin Wang, Shun An, Yan He, and Jianping Yuan*
Guangdong Ocean University
Abstract
This paper focuses on the maximum likelihood estimation for bilinear systems in the presence of colored noise. The state variables in the model is eliminated and an input-output expression is provided. The input-output data of the system is filtered by an estimated noise transfer function, and the system is transformed into two subsystems. A filtering based maximum likelihood recursive least squares algorithm is proposed to strengthen the identification accuracy and improve computational efficiency. The superior performance of the developed methods are demonstrated by numerical simulations.
Keywords: Bilinear system, data filtering, least squares, maximum likelihood.
Vol. 22, No. 12, pp. 3545~3811
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