Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays

The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are available.By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.

Novel delay-dependent stability criteria for neural networks with interval time-varying delay

The problem of delay-dependent asymptotic stability for neurM networks with interval time-varying delay is investigated. Based on the idea of delay decomposition method, a new type of Lyapunov Krasovskii functional is constructed. Several novel delay-dependent stability criteria are presented in terms of linear matrix inequality by using the Jensen integral inequality and a new convex combination technique. Numerical examples are given to demonstrate that the proposed method is effective and less conservative.

Robust stability analysis for Markovian jumping stochastic neural networks with mode-dependent time-varying interval delay and multiplicative noise

This paper is concerned with the problem of robust stability for a class of Markovian jumping stochastic neural networks （MJSNNs） subject to mode-dependent time-varying interval delay and state-multiplicative noise. Based on the Lyapunov-Krasovskii functional and a stochastic analysis approach, some new delay-dependent sufficient conditions are obtained in the linear matrix inequality （LMI） format such that delayed MJSNNs are globally asymptotically stable in the mean-square sense for all admissible uncertainties. An important feature of the results is that the stability criteria are dependent on not only the lower bound and upper bound of delay for all modes but also the covariance matrix consisting of the correlation coefficient. Numerical examples are given to illustrate the effectiveness.

Global Exponential Stability of Almost Periodic Solution of Cellular Neural Networks with Time-Varying Delays

In this paper, global exponential stability of almost periodic solution of cellular neural networks with time-varing delays （CNNVDs） is considered. By using the methods of the topological degree theory and generalized Halanay inequality, a few new applicable criteria are established for the existence and global exponential stability of almost periodic solution. Some previous results are improved and extended in this letter and one example is given to illustrate the effectiveness of the new results.

Stability analysis of cellular neural networks with time-varying delay

The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the feasibility of some linear matrix inequalities, which can be checked easily by resorting to the recently developed interior-point algorithms. Based on the Finsler Lemma, it is theoretically proved that the proposed stability criteria are less conservative than some existing results.

Global exponential stability analysis of cellular neural networks with multiple time delays

Global exponential stability problems are investigated for cellular neural networks （CNN） with multiple time-varying delays. Several new criteria in linear matrix inequality form or in algebraic form are presented to ascertain the uniqueness and global exponential stability of the equilibrium point for CNN with multiple time-varying delays and with constant time delays. The proposed method has the advantage of considering the difference of neuronal excitatory and inhibitory effects, which is also computationally efficient as it can be solved numerically using the recently developed interior-point algorithm or be checked using simple algebraic calculation. In addition, the proposed results generalize and improve upon some previous works. Two numerical examples are used to show the effectiveness of the obtained results.

Exponential convergence and stability of delayed fuzzy cellular neural networks with time-varying coefficients

In this paper, the dynamic behaviors of fuzzy cellular neural networks （FCNNs） with time-varying coefficients and delays are considered. Some criteria are established to ensure the exponential convergence or exponential stability of such neural networks. The effectiveness of obtained results is illustrated by a numerical example.

The anti-periodic oscillations of shunting inhibitory cellular neural networks with time-varying delays and continuously distributed delays

Purpose – The purpose of this paper is to study the existence and exponential stability of anti-periodicsolutions of a class of shunting inhibitory cellular neural networks (SICNNs) with time-varying delays andcontinuously distributed delays.Design/methodology/approach – The inequality technique and Lyapunov functional method are applied.Findings – Sufficient conditions are obtained to ensure that all solutions of the networks convergeexponentially to the anti-periodic solution, which are new and complement previously known results.Originality/value – There are few papers that deal with the anti-periodic solutions of delayed SICNNs withthe form negative feedback – aij(t)αij(xij(t)).

区间时变细胞神经网络的全局鲁棒指数稳定性

研究了一类区间时变扰动、变时滞细胞神经网络的全局鲁棒指数稳定性问题．利用Leibniz-Newton公式对原系统进行模型变换,并分析了变换模型和原始模型的等价性．基于变换模型,运用线性矩阵不等式的方法,通过选择适当的Lyapunov-Krasovskii泛函,推导了该系统全局鲁棒指数稳定的时滞相关的充分条件．通过数值实例将所得结果与前人的结果相比较,表明了本文所提出的稳定判据具有更低的保守性．

S-分布时滞区间细胞神经网络的全局渐近鲁棒稳定性

研究了一类具有S-分布时滞的区间细胞神经网络的全局渐近鲁棒稳定性问题,得到了实用有效的判别准则并给出了实例.

一类含脉冲的随机区间细胞神经网络模型的周期解和稳定性

讨论了一类含脉冲的S-分布时滞随机区间细胞神经网络的周期解和指数稳定性问题。利用随机分析的知识、不等式技巧和Poincaré压缩映像理论,研究了系统周期解的存在性和稳定性,得到了S-分布时滞随机细胞神经网络周期解的存在性和全局p阶指数稳定性的新代数判据,同时对周期解的指数收敛率进行了估计,最后通过例子说明了结果的实用性和有效性。

区间时变细胞神经网络周期解的鲁棒指数稳定性

本文研究一类区间时变扰动神经网络系统周期解的鲁棒指数稳定性.获得了一系列关于鲁棒指数稳定性的判据.

区间CNN网络鲁棒稳定分析

研究了连接权矩阵为区间矩阵的细胞神经网络(CNN)的鲁棒稳定性,得到了该网络系统鲁棒稳定的若干充分判据.

具有反应扩散和区间时滞的不确定细胞神经网络新的时滞依赖稳定性准则

考虑时滞、反应扩散、不确定项对细胞神经网络稳定性的影响是非常必要的。通过引入区间时滞,构造新的Lyapunov泛函,运用Jenson不等式和串联补偿技术,得到了系统保持稳定新的判据,并以线性矩阵不等式(LMI)呈现,且这些判据适合时滞的快慢变化,减少了系统的保守性。

S-分布时滞随机区间细胞神经网络的全局指数鲁棒稳定性

利用Lyapunov稳定性理论和随机分析的方法,给出了在均方意义下系统全局指数鲁棒稳定性的判据,并且给出了几乎必然指数稳定性的代数判据,通过仿真例子说明结果的实用性。

Influence of time delay on weighted pseudo-almost periodic dynamics in SICNNs

Purpose–The purpose of this paper is to investigate the weighted pseudo-almost periodic solutions of shunting inhibitory cellular neural networks(SICNNs)with time-varying delays and distributed delays.Design/methodology/approach–The principle of weighted pseudo-almost periodic functions and some new mathematical analysis skills are applied.Findings–A set of sufficient criteria which guarantee the existence and exponential stability of the weighted pseudo-almost periodic solutions of the considered SICNNs are established.Originality/value–The derived results of this paper are new and complement some earlier works.The innovation of this paper concludes two points:a new sufficient criteria guaranteeing the existence and exponential stability of the weighted pseudo-almost periodic solutions of SICNNs are established;and the ideas of this paper can be applied to investigate some other similar neural networks.