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 s...

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Vydáno v:International journal of control, automation, and systems Ročník 21; číslo 1; s. 151 - 160
Hlavní autoři: Wang, Longjin, An, Shun, He, Yan, Yuan, Jianping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.01.2023
Springer Nature B.V
제어·로봇·시스템학회
Témata:
Algorithms
Control
Engineering
Filtration
Least squares
Mathematical models
Maximum likelihood estimation
Mechatronics
Methods
Nonlinear systems
Numerical methods
Ocean engineering
Parameter estimation
Parameter identification
Regular Papers
Robotics
Signal processing
Stochastic systems
Subsystems
Transfer functions
Variables
제어계측공학
data filtering
Bilinear system
least squares
maximum likelihood
ISSN:1598-6446, 2005-4092
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Shrnutí: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.
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http://link.springer.com/article/10.1007/s12555-021-0923-1
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-0923-1