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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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