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dc.contributor.authorZhang, Zhijunen_US
dc.contributor.authorSong, Yatingen_US
dc.contributor.authorZheng, Lunanen_US
dc.contributor.authorLuo, Yameien_US
dc.identifier.citationZhang, 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.
dc.description.abstractNonlinear 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.en_US
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.rights© 2023 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleA jump-gain integral recurrent neural network for solving noise-disturbed time-variant nonlinear inequality problemsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchContinental-NTU Corporate Laben_US
dc.subject.keywordsGlobal Convergenceen_US
dc.description.acknowledgementThis work was supported in part by the National Natural Science Foundation under Grant 61976096, in part by the National High-Level Talents Special Support Program (Youth Talent of Technological Innovation of Ten-Thousands Talents Program) under Grant C7220060,in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020B1515120047, in part by the Guangdong Foundation forDistinguished Young Scholars under Grant 2017A030306009, in part by the Guangdong Special Support Program under Grant 2017TQ04X475, in part by the SCUT-Tianxiagu Joint Lab Funding under Grant x2zdD8212590, in part by the Pazhou Lab Young Scholar Program under Grant PZL2021KF0015,in part by the National Key Research and Development Program of China under Grant 2017YFB1002505, in part by the Guangdong Key Researchand Development Program under Grant 2018B030339001, and in part by the Guangdong Natural Science Foundation Research Team Program under Grant 1414060000024.en_US
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