Liu, T, Chen, S, Li, K orcid.org/0000-0001-6657-0522 et al. (2 more authors) (2023) Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression. IEEE Transactions on Cybernetics. ISSN 2168-2267
Abstract
Multioutput regression of nonlinear and nonstationary data is largely understudied in both machine learning and control communities. This article develops an adaptive multioutput gradient radial basis function (MGRBF) tracker for online modeling of multioutput nonlinear and nonstationary processes. Specifically, a compact MGRBF network is first constructed with a new two-step training procedure to produce excellent predictive capacity. To improve its tracking ability in fast time-varying scenarios, an adaptive MGRBF (AMGRBF) tracker is proposed, which updates the MGRBF network structure online by replacing the worst performing node with a new node that automatically encodes the newly emerging system state and acts as a perfect local multioutput predictor for the current system state. Extensive experimental results confirm that the proposed AMGRBF tracker significantly outperforms existing state-of-the-art online multioutput regression methods as well as deep-learning-based models, in terms of adaptive modeling accuracy and online computational complexity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Multioutput gradient radial basis function (MGRBF) network , multivariate nonlinear and nonstationary regression , online adaptive tracking , two-step training |
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: | 06 Jan 2023 10:31 |
Last Modified: | 17 May 2023 01:24 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TCYB.2023.3235155 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194834 |