Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem

Quadratic 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 suppressi...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 35; no. 11; pp. 15825 - 15833
Main Authors: Dai, Jianhua, Luo, Liu, Xiao, Lin, Jia, Lei, Cao, Penglin, Li, Jichun, Krasnogor, Natalio, Wang, Yaonan
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2024
Subjects:
Adaptation models
Convergence
Fuzzy logic
Fuzzy logic system (FLS)
fuzzy neural network
Mathematical models
quadratic programming with equality constraint (QPEC)
Recurrent neural networks
Robustness
Stability analysis
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Quadratic 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.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3290030