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Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects.具有随机扰动和脉冲效应的不确定时滞神经网络的鲁棒指数稳定性。
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Robust stability analysis of delayed Takagi-Sugeno fuzzy Hopfield neural networks with discontinuous activation functions.具有不连续激活函数的时滞 Takagi-Sugeno 模糊 Hopfield 神经网络的鲁棒稳定性分析。
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具有多个时变时滞的递归神经网络的指数输入状态稳定性。

Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays.

机构信息

Department of Mathematics, Key Laboratory for Optimization and Control of Ministry of Education, Chongqing Normal University, Chongqing, 400047 China.

Department of Mathematics, Chongqing Normal University, Chongqing, 400047 China.

出版信息

Cogn Neurodyn. 2014 Feb;8(1):47-54. doi: 10.1007/s11571-013-9258-9. Epub 2013 Jun 15.

DOI:10.1007/s11571-013-9258-9
PMID:24465285
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3890088/
Abstract

In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov-Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results.

摘要

本文研究了一类具有多个时变时滞的递归神经网络模型的输入状态稳定性问题。利用李雅普诺夫-克拉索夫斯基泛函方法和线性矩阵不等式技术，首先得到了保证时滞网络系统指数输入状态稳定性的一些充分条件。给出了两个数值实例及其仿真结果，以验证所得结果的有效性。