Maximum Likelihood Recursive Generalized Extended Least Squares Estimation Methods for a Bilinear-parameter Systems with ARMA Noise Based on the Over-parameterization Model

Maximum likelihood methods have wide applications in system modeling and parameter estimation. For the purpose of improving the precision of parameter estimation, this paper presents a maximum likelihood recursive generalized extended least squares (ML-RLS) algorithm for a bilinear-parameter system...

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Bibliographic Details
Published in:	International journal of control, automation, and systems Vol. 20; no. 8; pp. 2606 - 2615
Main Authors:	Liu, Haibo, Wang, Junwei, Ji, Yan
Format:	Journal Article
Language:	English
Published:	Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.08.2022 Springer Nature B.V 제어·로봇·시스템학회
Subjects:	Algorithms Autoregressive moving average Control Control Theory and Applications Engineering Least squares Mathematical models Mechatronics Parameter estimation Parameter identification Parameterization Robotics 제어계측공학 Bilinear-parameter system parameter estimation recursive identification least squares maximum likelihood
ISSN:	1598-6446, 2005-4092
Online Access:	Get full text
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