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Lookup NU author(s): Jiajie Luo, Dr Jichun Li
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© 2024 IEEE. Solving linear matrix inequality (LMI) is crucial across diverse fields, and the emergence of zeroing neural networks (ZNN) presents a novel solution for the time-varying LMI (TV-LMI) challenge. However, the application of ZNN to solve the time-varying complex-valued LMI (TVCV-LMI) problem remains unexplored. Therefore, we introduce a novel fuzzy-parameter ZNN (FP-ZNN) model in this study to tackle the TVCV-LMI problem. With the introduction of fuzzy logic system (FLS), the FP-ZNN model is able to adjust the fuzzy convergence parameter (FCP) in a real-time manner, responding to any change in the system error and achieving the best performance. We also use an exponential activation function (EAF) in our study, which makes the FP-ZNN model fixed-time stable. To verify and illustrate the superior features of the elegant FPZNN model, detailed theoretical analysis, together with numerical experiments, are provided, and the results emphasize the fixed-time stability and adaptiveness of the FP-ZNN model further. As a novel approach, we provide an elegant solution to the TVCV-LMI problem in this paper.
Author(s): Luo J, Li J, Holderbaum W, Li J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE International Conference on Cybernetics and Intelligent Systems (CIS 2024) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM 2024)
Year of Conference: 2024
Pages: 543-548
Online publication date: 16/09/2024
Acceptance date: 02/04/2018
ISSN: 2326-8239
Publisher: IEEE
URL: https://doi.org/10.1109/CIS-RAM61939.2024.10672985
DOI: 10.1109/CIS-RAM61939.2024.10672985
Library holdings: Search Newcastle University Library for this item
ISBN: 9798350364194
