Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models

Yihong Zhou; Qinyao Liu; Dan Yang

doi:10.1007/978-981-96-1777-7_22

Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models

Yihong Zhou^*, Qinyao Liu, Dan Yang

^*Corresponding author for this work

Department of Mechatronics and Robotics

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

This paper studies the parameter estimation problem for a class of separable nonlinear models, i.e., the RBF-ARX models. By exploiting the parameter separability property inherent in the RBF-ARX models, two gradient-based iterative sub-algorithms are derived to individually estimate the linear and nonlinear parameters based on the iterative search. Then a decomposition coordination-based iterative algorithm is proposed by integrating the sub-algorithms, which realizes high-precision iterative identification of all parameters. The effectiveness of the proposed algorithm is verified by a simulation example.

Original language	English
Title of host publication	Proceedings of the 16th International Conference on Modelling, Identification and Control, ICMIC 2024
Editors	Qiang Chen, Tingli Su, Peng Liu, Weicun Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	194-200
Number of pages	7
ISBN (Print)	9789819617760
DOIs	https://doi.org/10.1007/978-981-96-1777-7_22
Publication status	Published - 2025
Event	16th International Conference on Modelling, Identification and Control, ICMIC 2024 - Datong, China Duration: 9 Aug 2024 → 11 Aug 2024

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1315 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	16th International Conference on Modelling, Identification and Control, ICMIC 2024
Country/Territory	China
City	Datong
Period	9/08/24 → 11/08/24

Keywords

iterative identification
parameter estimation
RBF-ARX

Access to Document

10.1007/978-981-96-1777-7_22

Cite this

Zhou, Y., Liu, Q., & Yang, D. (2025). Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models. In Q. Chen, T. Su, P. Liu, & W. Zhang (Eds.), Proceedings of the 16th International Conference on Modelling, Identification and Control, ICMIC 2024 (pp. 194-200). (Lecture Notes in Electrical Engineering; Vol. 1315 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-1777-7_22

Zhou, Yihong ; Liu, Qinyao ; Yang, Dan. / Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models. Proceedings of the 16th International Conference on Modelling, Identification and Control, ICMIC 2024. editor / Qiang Chen ; Tingli Su ; Peng Liu ; Weicun Zhang. Springer Science and Business Media Deutschland GmbH, 2025. pp. 194-200 (Lecture Notes in Electrical Engineering).

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abstract = "This paper studies the parameter estimation problem for a class of separable nonlinear models, i.e., the RBF-ARX models. By exploiting the parameter separability property inherent in the RBF-ARX models, two gradient-based iterative sub-algorithms are derived to individually estimate the linear and nonlinear parameters based on the iterative search. Then a decomposition coordination-based iterative algorithm is proposed by integrating the sub-algorithms, which realizes high-precision iterative identification of all parameters. The effectiveness of the proposed algorithm is verified by a simulation example.",

keywords = "iterative identification, parameter estimation, RBF-ARX",

author = "Yihong Zhou and Qinyao Liu and Dan Yang",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 16th International Conference on Modelling, Identification and Control, ICMIC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",

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doi = "10.1007/978-981-96-1777-7_22",

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Zhou, Y, Liu, Q & Yang, D 2025, Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models. in Q Chen, T Su, P Liu & W Zhang (eds), Proceedings of the 16th International Conference on Modelling, Identification and Control, ICMIC 2024. Lecture Notes in Electrical Engineering, vol. 1315 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 194-200, 16th International Conference on Modelling, Identification and Control, ICMIC 2024, Datong, China, 9/08/24. https://doi.org/10.1007/978-981-96-1777-7_22

Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models. / Zhou, Yihong; Liu, Qinyao; Yang, Dan.
Proceedings of the 16th International Conference on Modelling, Identification and Control, ICMIC 2024. ed. / Qiang Chen; Tingli Su; Peng Liu; Weicun Zhang. Springer Science and Business Media Deutschland GmbH, 2025. p. 194-200 (Lecture Notes in Electrical Engineering; Vol. 1315 LNEE).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AB - This paper studies the parameter estimation problem for a class of separable nonlinear models, i.e., the RBF-ARX models. By exploiting the parameter separability property inherent in the RBF-ARX models, two gradient-based iterative sub-algorithms are derived to individually estimate the linear and nonlinear parameters based on the iterative search. Then a decomposition coordination-based iterative algorithm is proposed by integrating the sub-algorithms, which realizes high-precision iterative identification of all parameters. The effectiveness of the proposed algorithm is verified by a simulation example.

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Zhou Y, Liu Q, Yang D. Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models. In Chen Q, Su T, Liu P, Zhang W, editors, Proceedings of the 16th International Conference on Modelling, Identification and Control, ICMIC 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 194-200. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-96-1777-7_22