Exponential Stability of Uncertain Stochastic Neural Networks with Markovian Switching

This paper is concerned with the exponential stability analysis problem for a class of uncertain stochastic neural networks with Markovian switching. The parameter uncertainties are assumed to be norm bounded. Based on Lyapunov–Krasovskii stability theory and the nonnegative semimartingale convergence theorem, delay-dependent and delay- independent sufficient stability conditions are established. It is also shown that the result in this paper cover some recently published works. Two examples are provided to demonstrate the usefulness of the proposed criteria.     

Almost Sure Exponential Stability of Recurrent Neural Networks With Markovian Switching

This paper presents new stability results for recurrent neural networks with Markovian switching. First, algebraic criteria for the almost sure exponential stability of recurrent neural networks with Markovian switching and without time delays are derived. The results show that the almost sure exponential stability of such a neural network does not require the stability of the neural network at every individual parametric configuration. Next, both delay-dependent and delay-independent criteria for the almost sure exponential stability of recurrent neural networks with time-varying delays and Markovian-switching parameters are derived by means of a generalized stochastic Halanay inequality. The results herein include existing ones for recurrent neural networks without Markovian switching as special cases. Finally, simulation results in three numerical examples are discussed to illustrate the theoretical results.     

Global Exponential Stability of Hybrid Non-autonomous Neural Networks with Markovian Switching

Neural Processing Letters - This paper discusses the global exponential stability for a class of hybrid non-autonomous neural networks (HNNNs) with Markovian switching, which includes the factors...     

On Global Stability of Delayed BAM Stochastic Neural Networks with Markovian Switching

In this paper, the stability analysis problem is investigated for stochastic bi-directional associative memory (BAM) neural networks with Markovian jumping parameters and mixed time delays. Both the global asymptotic stability and global exponential stability are dealt with. The mixed time delays consist of both the discrete delays and the distributed delays. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, we employ the Lyapunov–Krasovskii stability theory and the Itô differential rule to establish sufficient conditions for the delayed BAM networks to be stochastically globally exponentially stable and stochastically globally asymptotically stable, respectively. These conditions are expressed in terms of the feasibility to a set of linear matrix inequalities (LMIs). Therefore, the global stability of the delayed BAM with Markovian jumping parameters can be easily checked by utilizing the numerically efficient Matlab LMI toolbox. A simple example is exploited to show the usefulness of the derived LMI-based stability conditions.     

On p-th Moment Exponential Stability for Stochastic Cellular Neural Networks with Distributed Delays

International Journal of Control, Automation and Systems - In this paper, a class of stochastic cellular neural networks with distributed delays are investigated. With the help of the method of...     

Attractor and Stochastic Boundedness for Stochastic Infinite Delay Neural Networks with Markovian Switching

First, we establish the stochastic LaSalle theorem for stochastic infinite delay differential equations with Markovian switching, from which some criterias on attraction are obtained. Then, by employing Lyapunov method and LaSalle-type theorem established above, we obtain some sufficient conditions ensuring the attractor and stochastic boundedness for stochastic infinite delay neural networks with Markovian switching. Finally, an example is also discussed to illustrate the efficiency of the obtained results.     

Exponential Stability of Positive Recurrent Neural Networks with Multi-proportional Delays

Neural Processing Letters - This paper presents some new results on the existence, uniqueness and generalized exponential stability of a positive equilibrium for positive recurrent neural networks...     

Exponential Stability for Neutral Stochastic Differential Delay Equations with Markovian Switching and Nonlinear Impulsive Effects

International Journal of Control, Automation and Systems - In this paper, the problems on the exponential stability in p-th (p ≥ 2)-moment and the almost sure exponential stability for...     

Delay-Dependent Robust Exponential Stability of Impulsive Markovian Jumping Reaction-Diffusion Cohen-Grossberg Neural Networks

This paper is devoted to investigating delay-dependent robust exponential stability for a class of Markovian jump impulsive stochastic reaction-diffusion Cohen-Grossberg neural networks (IRDCGNNs) with mixed time delays and uncertainties. The jumping parameters, determined by a continuous-time, discrete-state Markov chain, are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing a Lyapunov–Krasovskii functional, and using poincarè inequality and the mathematical induction method, several novel sufficient criteria ensuring the delay-dependent exponential stability of IRDCGNNs with Markovian jumping parameters are established. Our results include reaction-diffusion effects. Finally, a Numerical example is provided to show the efficiency of the proposed results.     

基于LMI的混合时滞随机神经网络指数稳定性

为研究具有混合时滞的随机反馈神经网络的平衡解的稳定性问题,基于Lyapunov稳定性理论及It随机微分公式计算了随机神经网络得到在均方意义下的全局指数稳定性.利用网络模型中混合时滞的形式特点构造了新型的Lyapunov-Krasovskii泛函,并借助矩阵不等式分析技巧建立了新型采用线性矩阵不等式形式的判别条件,较已有采用矩阵范数形式的判别条件放宽了要求.线性矩阵不等式可以利用Matlab中提供的线性矩阵不等式进行计算验证,使得所得判别条件更加实用.最后给出了数值证明判别条件的有效性.     

Stability Analysis of Discrete-Time Recurrent Neural Networks With Stochastic Delay

This paper is concerned with the stability analysis of discrete-time recurrent neural networks (RNNs) with time delays as random variables drawn from some probability distribution. By introducing the variation probability of the time delay, a common delayed discrete-time RNN system is transformed into one with stochastic parameters. Improved conditions for the mean square stability of these systems are obtained by employing new Lyapunov functions and novel techniques are used to achieve delay dependence. The merit of the proposed conditions lies in its reduced conservatism, which is made possible by considering not only the range of the time delays, but also the variation probability distribution. A numerical example is provided to show the advantages of the proposed conditions.      

Delay Dependent Exponential Stability for Fuzzy Recurrent Neural Networks with Interval Time-Varying Delay

In this paper, the problem of delay-dependent exponential stability for fuzzy recurrent neural networks with interval time-varying delay is investigated. The delay interval has been decomposed into multiple non equidistant subintervals, on these interval Lyapunov-Krasovskii functionals (LKFs) are constructed to study stability analysis. Employing these LKFs, an exponential stability criterion is proposed in terms of Linear Matrix Inequalities (LMIs) which can be easily solved by MATLAB LMI toolbox. Numerical example is given to illustrate the effectiveness of the proposed method.     

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.     

Exponential Synchronization of Stochastic Memristive Recurrent Neural Networks Under Alternate State Feedback Control

International Journal of Control, Automation and Systems - This paper solves the exponential synchronization problem of two memristive recurrent neural networks with both stochastic disturbance and...     

具有状态时滞的Markov切换系统的指数稳定性新结果

考虑具有状态时滞的Markov切换系统的均方指数稳定性分析问题. 为此, 我们构造了一类较为一般的与模态相关的Lyapunov-Krasovskii泛函, 并利用Markonv过程的统计性质计算泛函的微分. 进而, 通过引入自由权矩阵建立了以线性矩阵不等式表述的稳定性准则. 仿真算例验证了方法的有效性.     

具有漏泄时滞的随机神经网络的均方指数稳定性

在实现复杂的人工神经网络模型的过程中,随机噪声是不可避免的。建立具有随机噪声干扰的神经网络模型不但是设计上的需要,而且能够更加真实地反映生物神经网络的特点。本文利用构造合适的Lyapunov泛函,应用It?微分公式及Jensen不等式性质等,研究了一类具有漏泄时滞的随机神经网络的动力学行为,得到了确保该系统均方指数稳定的充分判别条件。最后, 通过两个数值计算的例子,说明所得结论的有效性。     

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.