Zhang, L, Li, K orcid.org/0000-0001-6657-0522, Du, D et al. (2 more authors) (2023) A regularised fast recursive algorithm for fraction model identification of nonlinear dynamic systems. International Journal of Systems Science, 54 (7). pp. 1616-1638. ISSN 0020-7721
Abstract
The fraction model has been widely used to represent a range of engineering systems. To accurately identify the fraction model is however challenging, and this paper presents a regularised fast recursive algorithm (RFRA) to identify both the true fraction model structure and the associated unknown model parameters. This is achieved first by transforming the fraction form to a linear combination of nonlinear model terms. Then the terms in the denominator are used to form a regularisation term in the cost function to offset the bias induced by the linear transformation. According to the structural risk minimisation principle based on the new cost function, the model terms are selected based on their contributions to the cost function and the coefficients are then identified recursively without explicitly solving the inverse matrix. The proposed method is proved to have low computational complexity. Simulation results confirm the efficacy of the method in fast identification of the true fraction models for the targeted nonlinear systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
Keywords: | Regularisation; fraction models; regularised fast recursive algorithm; nonlinear model identification |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Mar 2023 13:59 |
Last Modified: | 08 Nov 2023 15:01 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/00207721.2023.2188983 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197088 |
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Licence: CC-BY-NC-ND 4.0