咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >Mean square exponential stabil... 收藏

Mean square exponential stabilization analysis of stochastic neural networks with saturated impulsive input

作     者:Deng, Hao Li, Chuandong Chang, Fei Wang, Yinuo 

作者机构:Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China 

出 版 物:《NEURAL NETWORKS》 (神经网络)

年 卷 期:2024年第170卷

页      面:127-135页

核心收录:

学科分类:1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术(可授工学、理学学位)] 10[医学] 

基  金:National Key Research and Devel-opment Project, China [2018AAA0100101] National Natural Science Foundation of P.R. China [61873213, 61633011] 

主  题:Saturated impulsive input Stochastic neural networks Average impulsive interval Impulsive density Attractive domain 

摘      要:The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid and the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分