Improved Results on Passivity Analysis of Uncertain Neural Networks with Time-Varying Discrete and Distributed Delays

In this paper, the passivity analysis problem is investigated for uncertain neural networks with time-varying discrete and distributed delays. Based on direct delay decomposition idea and free-weighting matrix approach, several new delay-dependent passive criterions are derived in terms of linear matrix inequalities (LMIs), which can be easily checked by the Matlab LMI toolbox. Numerical examples show that the obtained results improve some existing ones.     

Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy $0≪d_{1}leq d(t) leq d_{2}$ and is described as $d(t)= d_{1}+h(t)$ with $0leq h(t) leq d_{2}-d_{1}$. Based on the idea of partitioning the lower bound $d_{1}$, new delay-dependent stability criteria are presented by constructing a novel Lyapunov–Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.      

Synchronization of Fractional Order Neutral Type Fuzzy Cellular Neural Networks with Discrete and Distributed Delays via State Feedback Control

Neural Processing Letters - The motivation behind this paper is to explore the issue of synchronization of fractional order neutral type fuzzy cellular neural networks with state feedback control....     

Global Robust Exponential Stability of Interval Neural Networks with Delays

In this Letter, based on globally Lipschitz continous activation functions, new conditions ensuring existence, uniqueness and global robust exponential stability of the equilibrium point of interval neural networks with delays are obtained. The delayed Hopfield network, Bidirectional associative memory network and Cellular neural network are special cases of the network model considered in this Letter. This revised version was published online in June 2006 with corrections to the Cover Date.     

Passivity Analysis of Non-autonomous Discrete-Time Inertial Neural Networks with Time-Varying Delays

Neural Processing Letters - This paper addresses the passivity problem for delayed non-autonomous discrete-time inertial neural networks (NDINN), including the discrete-time switched inertial...     

含离散和分布时变时延神经网络系统的指数稳定性

针对一类含有离散和分布时延神经网络,在神经激活函数较弱的约束条件下,通过定义一个更具一般性的Lyapunov泛函,使用凸组合技术,得到了新的基于线性矩阵不等式表示的指数稳定性判据.与现有结果相比,这些判据具有较小的保守性.仿真算例表明,得到的结果是有效的且保守性小.     

一类时滞神经网络的全局指数稳定性

利用M-矩阵和拓扑学等有关知识,通过构建向量李雅普诺夫函数,研究了一类包含分布时滞和可变时滞的神经网络的平衡点的存在性、唯一性及其全局指数稳定性。在没有假定激励函数有界、可微的情况下,得到了该类神经网络平衡点的存在性、唯一性及其在平衡点全局指数稳定的充分判据。该判据计算简便,且与时间滞后量无关,便于在实践中应用。文中给出了一个算例。     

Global Asymptotic Stability of Fuzzy Cellular Neural Networks with Unbounded Distributed Delays

Asymptotic stability problem of a class of fuzzy cellular neural networks with unbounded distributed delays is studied. New stability criteria are derived by employing a Lyapunov-Krasovskii functional and using LMI approach. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.     

Passivity Analysis of Stochastic Memristor-Based Complex-Valued Recurrent Neural Networks with Mixed Time-Varying Delays

Neural Processing Letters - In this paper, the passivity analysis of stochastic memristor-based complex-valued recurrent neural networks (SMCVRNNs) with discrete and distributed time-varying delays...     

Robust Passivity and Stability Analysis of Uncertain Complex-Valued Impulsive Neural Networks with Time-Varying Delays

Neural Processing Letters - In this article, we investigate the robust passivity and stability analysis of uncertain complex-valued impulsive neural network (UCVINN) models with time-varying...     

Global Asymptotic Stability Analysis of Neural Networks with Time-Varying Delays

Global asymptotic stability of the equilibrium point of neural networks with time-varying delays is considered in this paper. By utilizing the Lyapunov--Razumikhin technique, some new sufficient conditions are given. The new criteria do not require the delay function to be differentiable and the activation functions to be bounded or monotone nondecreasing. The results presented here are less restrictive and conservative than those given in the earlier references. Two examples are discussed to compare the present results with the existing ones.     

Global Robust Exponential Stability of Uncertain Neutral High-Order Stochastic Hopfield Neural Networks with Time-Varying Delays

In this paper, a class of uncertain neutral high-order stochastic Hopfield neural networks with time-varying delays is investigated. By using Lyapunov-Krasovskii functional and stochastic analysis approaches, new and less conservative delay-dependent stability criteria is presented in terms of linear matrix inequalities to guarantee the neural networks to be globally robustly exponentially stable in the mean square for all admissible parameter uncertainties and stochastic perturbations. Numerical simulations are carried out to illustrate the main results.     

Global Exponential Stability for Impulsive BAM Neural Networks with Distributed Delays on Time Scales

In this paper, by utilizing the time scale calculus theory, topological degree theory and Hölder’s inequality on time scales, we analyze a class of impulsive BAM neural networks with distributed delays on time scales. Some sufficient conditions are obtained to ensure the existence, uniqueness and the global exponential stability of the equilibrium point. Finally, an example is provided to demonstrate the effectiveness of the results.     

On Robust Exponential Periodicity of Interval Neural Networks with Delays

In this Letter, based on globally Lipschitz continous activation functions, new conditions ensuring existence, uniqueness and global robust exponential stability of the periodic solution of interval-delayed neural networks with periodic input are obtained. All the results obtained are generalizations of some resent results reported in the literature for neural networks with constant input.     

Finite-Time Stabilization of Neutral Hopfield Neural Networks with Mixed Delays

Neural Processing Letters - In this article, we investigate the problem of finite time stabilization (FTS) of neutral Hopfield neural networks (NHNNs) with mixed delays including infinite...     

变时滞神经网络的全局指数稳定性

利用M矩阵理论,同构理论以及不等式技巧,研究了一类变时滞神经网络平衡点的存在性和惟一性问题。同时利用M矩阵理论,反证法以及不等式技巧,得到了变时滞神经网络系统惟一的平衡点的全局指数稳定性的充分条件。通过判断由神经网络的权系数、自反馈函数以及激励函数构造的矩阵是否为M矩阵,即可以检验该变时滞神经网络系统的全局指数稳定性。该判据易于用Matlab进行检验,最后给出一个仿真示例进一步证明了判据的有效性。     

Robustly Exponential Stability Analysis for Discrete-Time Stochastic Neural Networks with Interval Time-Varying Delays

This paper deals with the problems of the global exponential stability and stabilization for a class of uncertain discrete-time stochastic neural networks with interval time-varying delay. By using the linear matrix inequality method and the free-weighting matrix technique, we construct a new Lyapunov–Krasovskii functional and establish new sufficient conditions to guarantee that the uncertain discrete-time stochastic neural networks with interval time-varying delay are globally exponential stable in the mean square. Furthermore, we extend our consideration to the stabilization problem for a class of discrete-time stochastic neural networks. Based on the state feedback control law, some novel delay-dependent criteria of the robust exponential stabilization for a class of discrete-time stochastic neural networks with interval time-varying delay are established. The controller gains are designed to ensure the global robust exponential stability of the closed-loop systems. Finally, numerical examples illustrate the effectiveness of the theoretical results we have obtained.     

Improved Delay-Derivative-Dependent Stability Analysis for Generalized Recurrent Neural Networks with Interval Time-Varying Delays

Neural Processing Letters - In this paper, the problem of delay-derivative-dependent stability analysis for generalized neural networks with interval time-varying delays is considered. First, we...