Auxiliary Model-Based Multiple Innovation Recursive Algorithm on Nonlinear Systems utilizing KeyTerm Separation Technique

This article primarily investigates the identification problem for two-input one-output nonlinear controlled autoregressive moving average system. Drawing from the auxiliary model identification idea and the key-term separation technique, this article utilizes the estimated parameters to construct a...

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Bibliographic Details
Main Authors: Fang Qiu, Yan Ji
Format: Article
Language:English
Published: Tamkang University Press 2025-02-01
Series:Journal of Applied Science and Engineering
Subjects:
Online Access:http://jase.tku.edu.tw/articles/jase-202509-28-09-0010
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Summary:This article primarily investigates the identification problem for two-input one-output nonlinear controlled autoregressive moving average system. Drawing from the auxiliary model identification idea and the key-term separation technique, this article utilizes the estimated parameters to construct an auxiliary model. It then uses its outputs to replace the unknown terms and derives an auxiliary model-based recursive extended least-squares algorithm. For further improving the parameter estimation accuracy, an auxiliary model-based multi-innovation extended least-squares algorithm is presented by using the multi-innovation identification theory. Finally, a simulation example is demonstrated to verify the effectiveness of the derived algorithms.
ISSN:2708-9967
2708-9975