Global asymptotic stability of BAM neural networks with distributed delays and reaction–diffusion terms

The global asymptotic stability of bi-directional associative memory neural networks with distributed delays and reaction–diffusion terms are studied by using the analysis technique and Lyapunov functional. A sufficient condition is proposed. Two numerical examples are given to show the correctness of our analysis.     

Global exponential stability of non-autonomous FCNNs with Dirichlet boundary conditions and reaction–diffusion terms

In this paper, we investigate the global exponential stability of non-autonomous fuzzy cellular neural networks (FCNNs) with Dirichlet boundary conditions and reaction–diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain some sufficient conditions for the uniqueness and global exponential stability of the equilibrium solution. The result is easy to check and plays an important role in the design and applications of globally exponentially stable fuzzy neural circuits. Finally, the utility of our result is illustrated via a numerical example.     

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.     

具有脉冲和无限时滞的模糊细胞神经网络的全局指数稳定性

In this paper,the global exponential stability of fuzzy cellular neural networks with impulses and infinite delays is investigated.Based on an impulsive delayed integro-differential inequality and the properties of fuzzy logic operation and M-matrix,an easily verified sufficient condition is obtained.Moreover,the exponential convergent rate for the fuzzy cellular neural networks with impulses and infinite delays is also given.An example is given to illustrate the effectiveness of our theoretical result.     

Stability analysis of impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms

In this paper, we investigate a class of impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. By establishing an integro-differential inequality with impulsive initial conditions and applying M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness, global exponential stability and global robust exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. An example is given to illustrate the results obtained here.     

Analysis of global exponential stability and periodic solutions of FCNNs with constant delays and time-varying delays

In this paper, we investigate a class of fuzzy cellular neural networks with constant delays and time-varying delays. By constructing suitable Lyapunov functional and employing Young inequality, we find sufficient conditions for the existence, uniqueness, global exponential stability of equilibrium, and the existence of periodic solutions of fuzzy cellular neural networks with time-varying delays. The results of this paper are new and they extend previously known results.     

Global exponential stability and existence of periodic solutions in BAM networks with delays and reaction–diffusion terms

Both exponential stability and periodic solutions are considered for a class of bi-directional associative memory (BAM) neural networks with delays and reaction–diffusion terms by constructing suitable Lyapunov functional and some analysis techniques. The general sufficient conditions are given ensuring the global exponential stability and existence of periodic solutions of BAM neural networks with delays and reaction–diffusion terms. These presented conditions are in terms of system parameters and have important leading significance in the design and applications of globally exponentially stable and periodic oscillatory neural circuits for BAM with delays and reaction–diffusion terms.     

具有脉冲和无限时滞的模糊细胞神经网络的全局指数稳定性

本文讨论了具有脉冲和无限时滞的模糊细胞神经网络的全局指数稳定性.通过建立一个脉冲时滞%积分微分不等式,以及模糊逻辑算子与M-矩阵的性质,不仅得到了系统全局指数稳定的充分条件,而且也给出了指数收敛速度.最后,所给的例子充分验证了文中所给出的充分条件的有效性.     

具传输时滞和变系数的模糊细胞神经网络的指数稳定性及周期解

介绍一类具传输时滞和变系数的模糊细胞神经网络(FCNN),通过使用线性矩阵不等式(LMI)和Lyapunov-Krasovskii泛函,研究它的周期解的存在性及全局指数稳定性,并获得一些充分条件。此外,给出一个实例说明结果是可行的。     

Dynamical behaviors of fuzzy reaction–diffusion periodic cellular neural networks with variable coefficients and delays

When modeling neural networks in a real world, not only diffusion effect and fuzziness cannot be avoided, but also self-inhibitions, interconnection weights, and inputs should vary as time varies. In this paper, we discuss the dynamical behaviors of delayed reaction–diffusion fuzzy cellular neural networks with varying periodic self-inhibitions, interconnection weights as well as inputs. By using Halanay’s delay differential inequality, M$M$-matrix theory and analytic methods, some new sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential stability of the periodic solution, and the exponentially convergent rate index is also estimated. In particular, the traditional assumption on the differentiability of the time-varying delays is no longer needed. The methodology developed in this paper is shown to be simple and effective for the exponential periodicity and stability analysis of neural networks with time-varying delays. Two examples are given to show the usefulness of the obtained results that are less restrictive than recently known criteria.     

Dynamical behaviors of fuzzy cellular neural networks with discrete time delays

This paper is concerned with the dynamical behavior of fuzzy cellular neural networks with discrete time delays. By constructing proper Lyapunov functional, applying inequality technique and the homeomorphism theory, some new sufficient conditions checking the existence, uniqueness of the equilibrium point and its global exponential stability are obtained for fuzzy cellular neural networks. The results of this paper are new and they complement previously known results, Moreover an example is given to show the effectiveness of the obtained results.     

Exponential stability of reaction–diffusion generalized Cohen–Grossberg neural networks with time-varying delays

The stability property of reaction–diffusion generalized Cohen–Grossberg neural networks (GDCGNNs) with time-varying delay are considered. Without assuming the monotonicity and differentiability of activation functions, nor symmetry of synaptic interconnection weights, delay independent and easily verifiable sufficient conditions to guarantee the exponential stability of an equilibrium solution associated with temporally uniform external inputs to the networks are obtained, by employing the method of variational parameter and inequality technique. One example is given to illustrate the theoretical results.     

Neural networks with discrete and distributed time-varying delays: A general stability analysis

In this paper, the global asymptotic and exponential stability are investigated for a class of neural networks with both the discrete and distributed time-varying delays. By using appropriate Lyapunov–Krasovskii functional and linear matrix inequality (LMI) technique, several delay-dependent sufficient conditions are obtained to guarantee the global asymptotic and exponential stability of the addressed neural networks. These conditions are expressed in terms of LMIs, and are dependent on both the discrete and distributed time delays. Therefore, the stability of the neural networks can be checked readily by resorting to the Matlab LMI toolbox. In addition, the proposed stability criteria do not require the monotonicity of the activation functions and the differentiability of the discrete and distributed time-varying delays, which means that our results generalize and further improve those in the earlier publications. A simulation example is given to show the effectiveness and less conservatism of the obtained conditions.     

Dynamics of fuzzy impulsive bidirectional associative memory neural networks with time-varying delays

Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy bidirectional associative memory (BAM) neural networks with impulsive effect and time-varying delays is investigated. The model of fuzzy impulsive BAM neural networks with time-varying delays established as a modified TS fuzzy model is new in which the consequent parts are composed of a set of impulsive BAM neural networks with time-varying delays. Further the exponential stability for fuzzy impulsive BAM neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique without tuning any parameters. In addition, an example is provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.     

Exponential stability and periodic solution for fuzzy BAM Neural networks with time varying delays

In this paper, a class of fuzzy BAM neural networks with time varying delays is discussed. By using the properties of M-matrix, Linear Matrix Inequality（LMI） approach and general Lyapunov-Krasovskii functional, some new sufficient conditions are derived to ensure the existence of periodic solutions and the global exponential stability of the fuzzy BAM neural networks with time varying delays. These results have important significance in the design of global exponential stable BAM networks with delays. Moreover, an example is given to illustrate that the conditions of the results in the paper are feasible.     

Global asymptotic stability of CNNs with impulses and multi‐proportional delays

This paper is devoted to global asymptotic stability of cellular neural networks with impulses and multi‐proportional delays. First, by means of the transformation v_i(t) = u_i(e^t), the impulsive cellular neural networks with proportional delays are transformed into impulsive cellular neural networks with the variable coefficients and constant delays. Second, we prove the global exponential stability of the latter by nonlinear measure, and that the exponential stability of the latter implies the asymptotic stability of the former. We furthermore provide a sufficient condition to the existence, uniqueness, and the global asymptotic stability of the equilibrium point of the former. Our results are generalizations of some existing ones. Finally, an example and its simulation are presented to illustrate effectiveness of our method. Copyright © 2015 John Wiley & Sons, Ltd.     

变时滞细胞神经网络的指数稳定的一个充分条件

本文我们研究了变时滞细胞神经网络的指数稳定性。利用分析技巧，获得了变时滞细胞神经网络的指数稳定的充分条件，改进了已有文献中的相应结果。     

New results for global stability of a class of neutral-type neural systems with time delays

This paper studies the global convergence properties of a class of neutral-type neural networks with discrete time delays. This class of neutral systems includes Cohen–Grossberg neural networks, Hopfield neural networks and cellular neural networks. Based on the Lyapunov stability theorems, some delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for this class of neutral-type systems are derived. It is shown that the results presented in this paper for neutral-type delayed neural networks are the generalization of a recently reported stability result. A numerical example is also given to demonstrate the applicability of our proposed stability criteria.     

具S-型分布时滞的模糊细胞神经网络的周期解及指数稳定性

研究了一类具S-型分布时滞的模糊细胞神经网络(FCNN)的周期解及全局指数稳定性问题.在不要求激励函数全局L ipsch itz条件下,通过使用指数型二分性和Schauder不动点定理以及构造Lyapunov函数,得到了模糊细胞神经网络模型周期解和指数稳定性的一些充分条件.此外,给出一个实例说明结果是可行的.     

A graph-theoretic approach to exponential stability of BAM neural networks with delays and reaction-diffusion

This paper deals with the problem of global exponential stability for bidirectional associate memory (BAM) neural networks with time-varying delays and reaction-diffusion terms. By using some inequality techniques, graph theory as well as Lyapunov stability theory, a systematic method of constructing a global Lyapunov function for BAM neural networks with time-varying delays and reaction-diffusion terms is provided. Furthermore, two different kinds of sufficient principles are derived to guarantee the exponential stability of BAM neural networks. Finally, a numerical example is carried out to demonstrate the effectiveness and applicability of the theoretical results.