Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects
Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects
复制标题
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
DOI：
10.1109/tnnls.2012.2192135
复制
发表时间：
2012-06-01
影响因子：
10.4
通讯作者：
Starzyk, Janusz A.
中科院分区：
文献类型：
--
作者：
Huang, Tingwen;Li, Chuandong;Starzyk, Janusz A.
研究了参数不确定性、随机扰动和脉冲对时滞神经网络全局稳定性的影响。利用Ito公式、李雅普诺夫函数和Halanay不等式，建立了几个均方稳定性判据，在参数不确定性和随机扰动受到良好约束的情况下，由这些判据可以估计脉冲的可行界.此外，该方法还可应用于一般的具有随机扰动和脉冲的微分系统。
This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.