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.     

Extended dissipative output-feedback control for discrete-time bidirectional associative memory neural networks via delay-partitioning approach

This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time-varying delays. To achieve this, the idea of the delay-partitioning approach is used, where the range of time-varying delay factors is partitioned into a finite number of equidistant subintervals. A delay-partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay-dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete-time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay-partitioning approach.     

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.     

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.     

Global dissipativity of memristor-based complex-valued neural networks with time-varying delays

Memristor is the new model two-terminal nonlinear circuit device in electronic circuit theory. This paper deals with the problem of global dissipativity and global exponential dissipativity for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays. Sufficient global dissipativity conditions are derived from the theory of M-matrix analysis, and the globally attractive set as well as the positive invariant set is established. By constructing Lyapunov–Krasovskii functionals and using a linear matrix inequality technique, some new sufficient conditions on global dissipativity and global exponential dissipativity of MCVNNs are derived. Finally, two numerical examples are presented to demonstrate the effectiveness of our proposed theoretical results.     

Exponential stability analysis for neutral BAM neural networks with time-varying delays and stochastic disturbances

This paper is concerned with the global exponential stability analysis problem for a class of neutral bidirectional associative memory (BAM) neural networks with time-varying delays and stochastic dist...     

Robust passivity analysis of a class of discrete-time stochastic neural networks

This paper addresses the passivity problem of a class of discrete-time stochastic neural networks with time-varying delays and norm-bounded parameter uncertainties. New delay-dependent passivity conditions are obtained by using a novel Lyapunov functional together with the linear matrix inequality approach. Numerical examples show the effectiveness of the proposed method.     

Anti-periodic solutions for HCNNs with time-varying delays in the leakage terms

In this paper, a class of high-order cellular neural networks model is considered with the introduction of time-varying delays in the leakage terms. By using differential inequality techniques, some very verifiable and practical delay-dependent criteria on the existence and global exponential stability of anti-periodic solution for the model are derived. Even for the model without leakage delays, the criteria are shown to be less conservative than many recent publications. Moreover, some examples and remarks are given to demonstrate the feasibility of our method.     

Exponential synchronization of stochastic chaotic neural networks with mixed time delays and Markovian switching

This paper studies the exponential synchronization problem for a class of stochastic perturbed chaotic neural networks with both Markovian jump parameters and mixed time delays. The mixed delays consist of discrete and distributed time-varying delays. At first, based on a Halanay-type inequality for stochastic differential equations, by virtue of drive-response concept and time-delay feedback control techniques, a delay-dependent sufficient condition is proposed to guarantee the exponential synchronization of two identical Markovian jumping chaotic-delayed neural networks with stochastic perturbation. Then, by utilizing the Jensen integral inequality and a novel Lemma, another delay-dependent criterion is established to achieve the globally stochastic robust synchronization. With some parameters being fixed in advance, these conditions can be solved numerically by employing the Matlab software. Finally, a numerical example with their simulations is provided to illustrate the effectiveness of the presented synchronization scheme.     

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．     

Global Dissipativity of Inertial Neural Networks with Proportional Delay via New Generalized Halanay Inequalities

This article is devoted to the global dissipativity of inertial neural networks with proportional delay. A novel generalized Halanay inequality which involves proportional delay is established. By constructing a new generalized Halanay inequality, several new explicit delay-independent conditions are derived in terms of linear matrix inequalities to ensure the global dissipativity of the considered system. Moreover, a new differential delay inequality which involves unbounded time-varying delay is considered. Due to the proportional delay is one type of unbounded time-varying delays, new analysis techniques can effectively avoid the difficulties caused by proportional delay by applying a new differential delay inequality. Especially, several novel delay-dependent sufficient conditions are obtained to guarantee the global dissipativity of the considered system. Finally, two simulations examples are provided to illustrate the validity of the proposed theoretical analysis.     

Global exponential convergence of periodic neural networks with time-varying delays

In this paper, the global exponential convergence of a general class of periodic neural networks with time-varying delays is investigated. Based on the theory of mixed monotone operator, a testable algebraic criteria for ascertaining global exponential convergence is derived. Furthermore, the rate of exponential convergence and bound of the networks are also estimated. Finally, a numerical example is given to show the effectiveness of the obtained results.     

Stability analysis of Takagi-Sugeno fuzzy cellular neural networks with time-varying delays.

This correspondence investigates the global exponential stability problem of Takagi-Sugeno fuzzy cellular neural networks with time-varying delays (TSFDCNNs). Based on the Lyapunov-Krasovskii functional theory and linear matrix inequality technique, a less conservative delay-dependent stability criterion is derived to guarantee the exponential stability of TSFDCNNs. By constructing a Lyapunov-Krasovskii functional, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is released in the proposed delay-dependent stability criterion. Two illustrative examples are provided to verify the effectiveness of the proposed results.     

Stability Analysis of Takagi–Sugeno Fuzzy Cellular Neural Networks With Time-Varying Delays

This correspondence investigates the global exponential stability problem of Takagi-Sugeno fuzzy cellular neural networks with time-varying delays (TSFDCNNs). Based on the Lyapunov-Krasovskii functional theory and linear matrix inequality technique, a less conservative delay-dependent stability criterion is derived to guarantee the exponential stability of TSFDCNNs. By constructing a Lyapunov-Krasovskii functional, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is released in the proposed delay-dependent stability criterion. Two illustrative examples are provided to verify the effectiveness of the proposed results     

Global exponential stability of neural networks with discrete and distributed delays and general activation functions on time scales

By employing time scale calculus theory, free weighting matrix method and linear matrix inequality (LMI) approach, several delay-dependent sufficient conditions are obtained to ensure the existence, uniqueness and global exponential stability of the equilibrium point for the neural networks with both infinite distributed delays and general activation functions on time scales. Both continuous-time and discrete-time neural networks are described under the same framework by the reported method. Illustrated numerical examples are given to show the effectiveness of the theoretical analysis. It is noteworthy that the activation functions are assumed to be neither bounded nor monotone.     

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.     

Robust Stability in Cohen–Grossberg Neural Network with both Time-Varying and Distributed Delays

In this article, the global exponential robust stability is investigated for Cohen–Grossberg neural network with both time-varying and distributed delays. The parameter uncertainties are assumed to be time-invariant and bounded, and belong to given compact sets. Applying the idea of vector Lyapunov function, M-matrix theory and analysis techniques, several sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential robust stability of the equilibrium point for the neural network. The methodology developed in this article is shown to be simple and effective for the exponential robust stability analysis of neural networks with time-varying delays and distributed delays. The results obtained in this article extend and improve a few recently known results and remove some restrictions on the neural networks. Three examples are given to show the usefulness of the obtained results that are less restrictive than recently known criteria.      

离散区间二型Tagaki-Sugeno模型时滞系统广义耗散控制设计

对带有时变时滞和外部扰动的一类离散区间二型Tagaki-Sugeno(T–S)模型非线性系统,研究了其广义耗散性能分析与状态反馈控制器的设计问题.与一型T–S模糊系统相比,区间二型模糊系统能更好地处理隶属函数中的不确定信息.首先,通过模型转换的方法,对系统的滞后状态进行变换,从而将时变时滞的不确定性从原系统中分离出.根据转换后的仅含定常时滞和具有有界误差范数的两个子系统,利用时滞依赖的李雅普诺夫-克拉索夫斯基泛函方法推导出了使系统渐近稳定并具有广义耗散性能的充分条件.接着,设计了保证闭环系统渐近稳定并具有广义耗散性能指标的状态反馈控制器.最后由数值仿真验证了设计方法的有效性.     

Novel robust stability criteria of discrete-time stochastic recurrent neural networks with time delay

The problem of robust global exponential stability is investigated for a class of stochastic uncertain discrete-time recurrent neural networks with time delay. In this paper, the midpoint of the time delay's variation interval is introduced, and the variation interval is divided into two subintervals. Then, by constructing a new Lyapunov–Krasovskii functional and checking its variation in the two subintervals, respectively, some novel delay-dependent stability criteria for the addressed neural networks are derived. Numerical examples are provided to show that the achieved conditions are less conservative than some existing ones in the literature.     

Extended dissipativity of generalised neural networks including time delays

This article explores the extended dissipativity conditions for generalised neural networks (GNNs) including interval time-varying delays. Extended dissipativity criterions are proposed by making proper Lyapunov–Krasovskii functional. The improved reciprocally convex combination and weighted integral inequality techniques are together applied in main results to establish the new extended dissipativity conditions of delayed GNNs. Finally, the feasibility and superiority of the proposed novel approach is clearly shown by numerical examples.