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Lookup NU author(s): Dr Jichun Li, Professor Natalio KrasnogorORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
IEEEQuadratic programming with equality constraint (QPEC) problems have extensive applicability in many industries as a versatile nonlinear programming modeling tool. However, noise interference is inevitable when solving QPEC problems in complex environments, so research on noise interference suppression or elimination methods is of great interest. This article proposes a modified noise-immune fuzzy neural network (MNIFNN) model and use it to solve QPEC problems. Compared with the traditional gradient recurrent neural network (TGRNN) and traditional zeroing recurrent neural network (TZRNN) models, the MNIFNN model has the advantage of inherent noise tolerance ability and stronger robustness, which is achieved by combining proportional, integral, and differential elements. Furthermore, the design parameters of the MNIFNN model adopt two disparate fuzzy parameters generated by two fuzzy logic systems (FLSs) related to the residual and residual integral term, which can improve the adaptability of the MNIFNN model. Numerical simulations demonstrate the effectiveness of the MNIFNN model in noise tolerance.
Author(s): Dai J, Luo L, Xiao L, Jia L, Cao P, Li J, Krasnogor N, Wang Y
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Networks and Learning Systems
Year: 2023
Pages: epub ahead of print
Online publication date: 07/07/2023
Acceptance date: 20/06/2023
Date deposited: 05/09/2023
ISSN (print): 2162-237X
ISSN (electronic): 2162-2388
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/TNNLS.2023.3290030
DOI: 10.1109/TNNLS.2023.3290030
PubMed id: 37418408
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