A jump-gain integral recurrent neural network for solving noise-disturbed time-variant nonlinear inequality problems

A jump-gain integral recurrent neural network for solving noise-disturbed time-variant nonlinear inequality problems

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 er...

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書目詳細資料
Main Authors:	Zhang, Zhijun, Song, Yating, Zheng, Lunan, Luo, Yamei
其他作者:	School of Electrical and Electronic Engineering
格式:	Article
語言:	English
出版:	2023
主題:	Engineering::Computer science and engineering Antidisturbance Global Convergence
在線閱讀:	https://hdl.handle.net/10356/170578
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

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