Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/170578
Title: A jump-gain integral recurrent neural network for solving noise-disturbed time-variant nonlinear inequality problems
Authors: Zhang, Zhijun
Song, Yating
Zheng, Lunan
Luo, Yamei
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Zhang, Z., Song, Y., Zheng, L. & Luo, Y. (2023). A jump-gain integral recurrent neural network for solving noise-disturbed time-variant nonlinear inequality problems. IEEE Transactions On Neural Networks and Learning Systems. https://dx.doi.org/10.1109/TNNLS.2023.3241207
Journal: IEEE Transactions on Neural Networks and Learning Systems
Abstract: Nonlinear inequalities are widely used in science and engineering areas, attracting the attention of many researchers. In this article, a novel jump-gain integral recurrent (JGIR) neural network is proposed to solve noise-disturbed time-variant nonlinear inequality problems. To do so, an integral error function is first designed. Then, a neural dynamic method is adopted and the corresponding dynamic differential equation is obtained. Third, a jump gain is exploited and applied to the dynamic differential equation. Fourth, the derivatives of errors are substituted into the jump-gain dynamic differential equation, and the corresponding JGIR neural network is set up. Global convergence and robustness theorems are proposed and proved theoretically. Computer simulations verify that the proposed JGIR neural network can solve noise-disturbed time-variant nonlinear inequality problems effectively. Compared with some advanced methods, such as modified zeroing neural network (ZNN), noise-tolerant ZNN, and varying-parameter convergent-differential neural network, the proposed JGIR method has smaller computational errors, faster convergence speed, and no overshoot when disturbance exists. In addition, physical experiments on manipulator control have verified the effectiveness and superiority of the proposed JGIR neural network.
URI: https://hdl.handle.net/10356/170578
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2023.3241207
Schools: School of Electrical and Electronic Engineering 
Research Centres: Continental-NTU Corporate Lab
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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