Existence and global exponential stability of periodic solution of cellular neural networks with time-varying delays

By using the continuation theorem of Mawhin's coincidence degree theory and Gronwall's inequality, some new sufficient conditions are obtained ensuring existence and global exponential stability of periodic solution of cellular neural networks with periodic coefficients and delays. These results are helpful to design globally exponentially stable and oscillatory cellular neural networks.     

Global exponential stability of nonautonomous neural networks with time-varying delays and reaction-diffusion terms

A class of nonautonomous neural networks with time-varying delays and reaction-diffusion terms is considered. By means of Lyapunov functionals and differential inequality techniques, criteria on global exponential stability of this model with the Neumann boundary conditions and the Dirichlet boundary conditions are derived, respectively. The results of this paper are new and they improve and generalize previously known results.     

Global exponential stability of high order recurrent neural network with time-varying delays

In this paper, global exponential stability of high order recurrent neural network with time-varying delay and bounded activation functions is investigated. Some improved conditions are obtained involving external input, connection weights, and time delays of recurrent neural network. Moreover, the location of the equilibrium point can be estimated. In addition, two examples are demonstrated to illustrate the effectiveness of the proposed criteria in comparison with some existing results.     

Global exponential stability in Lagrange sense for recurrent neural networks with time delays

In this paper, we study the global exponential stability in Lagrange sense for continuous recurrent neural networks (RNNs) with multiple time delays. Three different types of activation functions are considered, which include both bounded and unbounded activation functions. By constructing appropriate Lyapunov-like functions, we provide easily verifiable criteria for the boundedness and global exponential attractivity of RNNs. These results can be applied to analyze monostable as well as multistable neural networks.     

Global exponential stability of impulsive discrete-time neural networks with time-varying delays

This paper studies the problem of global exponential stability and exponential convergence rate for a class of impulsive discrete-time neural networks with time-varying delays. Firstly, by means of the Lyapunov stability theory, some inequality analysis techniques and a discrete-time Halanay-type inequality technique, sufficient conditions for ensuring global exponential stability of discrete-time neural networks are derived, and the estimated exponential convergence rate is provided as well. The obtained results are then applied to derive global exponential stability criteria and exponential convergence rate of impulsive discrete-time neural networks with time-varying delays. Finally, numerical examples are provided to illustrate the effectiveness and usefulness of the obtained criteria.     

Existence and global exponential stability of periodic solution for impulsive Cohen-Grossberg-type BAM neural networks with continuously distributed delays

This paper is concerned with the existence and global exponential stability of periodic solution for a class of impulsive Cohen-Grossberg-type BAM neural networks with continuously distributed delays. Some sufficient conditions ensuring the existence and global exponential stability of periodic solution are derived by constructing a suitable Lyapunov function and a new differential inequality. The proposed method can also be applied to study the impulsive Cohen-Grossberg-type BAM neural networks with finite distributed delays. The results in this paper extend and improve the earlier publications. Finally, two examples with numerical simulations are given to demonstrate the obtained results.     

Global exponential stability of Hopfield neural networks with distributed delays

In this paper, dynamical behaviors of Hopfield neural networks system with distributed delays were studied. By using contraction mapping principle and differential inequality technique, a sufficient condition was obtained to ensure the existence uniqueness and global exponential stability of the equilibrium point for the model. Here we point out that our methods, which are different from previous known results, base on the contraction mapping principle and inequality technique. Two remarks were also worked out to demonstrate the advantage of our results.     

Further analysis on global robust exponential stability of neural networks with time-varying delays

In this paper, an approach based on H-matrix theory and Halanay inequality is developed to present a sufficient condition for global robust exponential stability of interval neural networks with time-varying delays. Theoretic analysis shows that our result includes a previous result derived in the literature. Finally, some numerical examples are given to show the effectiveness of the obtained results.     

On exponential stability analysis for neural networks with time-varying delays and general activation functions

This paper is concerned with the exponential stability analysis for a class of cellular neural networks with both interval time-varying delays and general activation functions. The boundedness assumption of the activation function is not required. The limitation on the derivative of time delay being less than one is relaxed and the lower bound of time-varying delay is not restricted to be zero. A new Lyapunov-Krasovskii functional involving more information on the state variables is established to derive a novel exponential stability criterion. The obtained condition shows potential advantages over the existing ones since no useful item is ignored throughout the estimate of upper bound of the derivative of Lyapunov functional. Finally, three numerical examples are included to illustrate the proposed design procedures and applications.     

Existence and stability of periodic solutions of high-order Hopfield neural networks with impulses and delays

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, the global exponential stability and periodicity are investigated for a class of delayed high-order Hopfield neural networks (HHNNs) with impulses, which are new and complement previously known results. Finally, an example with numerical simulation is given to show the effectiveness of the proposed method and results. The numerical simulation shows that our models can occur in many forms of complexities including periodic oscillation and the Gui chaotic strange attractor.     

Global exponential stability of Cohen-Grossberg neural networks with variable delays

A class of generalized Cohen-Grossberg neural networks（CGNNs） with variable de- lays are investigated. By introducing a new type of Lyapunov functional and applying the homeomorphism theory and inequality technique, some new conditions axe derived ensuring the existence and uniqueness of the equilibrium point and its global exponential stability for CGNNs. These results obtained are independent of delays, develop the existent outcome in the earlier literature and are very easily checked in practice.     

Stability analysis for periodic solution of BAM neural networks with discontinuous neuron activations and impulses

In this paper, we present a general class of BAM neural networks with discontinuous neuron activations and impulses. By using the fixed point theorem in differential inclusions theory, we investigate the existence of periodic solution for this neural network. By constructing the suitable Lyapunov function, we give a sufficient condition which ensures the uniqueness and global exponential stability of the periodic solution. The results of this paper show that the Forti’s conjecture is true for BAM neural networks with discontinuous neuron activations and impulses. Further, a numerical example is given to demonstrate the effectiveness of the results obtained in this paper.     

Exponential stability of periodic neural networks with impulsive effects and time-varying delays

By employing Young inequality and constructing suitable Liapunov functions, we investigate the existence and globally exponential stability of periodic neural networks with impulses and time-varying delays. The results extend and improve some earlier ones [1], [5] and [12]. An illustrative example and simulations are given to show the validity of the main results.     

Periodic solution to BAM-type Cohen-Grossberg neural network with time-varying delays

By using the continuation theorem of Mawhin’s coincidence degree theory and the Liapunov func tional method, some sufficient conditions are obtained to ensure the existence, uniqueness and the global exponential stability of the periodic solution to the BAM-type Cohen-Grossberg neural networks involving timevarying delays.     

具有时滞的双向联想记忆神经网络的指数稳定性和周期解的存在性

通过构造Lyapunov泛函和运用不等式分析技巧，得到了判断一类时滞双向联想记忆神经网络平衡点的指数稳定性和周期解的存在性的充分条件，为综合设计具有时滞的双向联想记忆神经网络提供了依据。     

Global asymptotic stability of BAM neural networks with mixed delays and impulses

In this paper, BAM neural networks with mixed delays and impulses are considered. A new set of sufficient conditions are derived by constructing suitable Lyapunov functional with matrix theory for the global asymptotic stability of BAM neural networks with mixed delays and impulses. Moreover, an example is also provided to illustrate the effectiveness of the results.     

Stability and periodicity in high-order neural networks with impulsive effects

In this paper, the global exponential stability and periodicity are investigated for a class generalized high-order neural networks with time delays and impulses; some sufficient conditions are derived for checking the global exponential stability and existence of periodic solutions for this system using the generalized Halanay inequality, mathematical induction and a fixed point theorem. The criteria given are easily verified and possess many adjustable parameters, which provides flexibility for the design and analysis of the system. Finally, an example with numerical simulation is given to show the effectiveness of the proposed method and results.     

Global exponential stability for shunting inhibitory CNNs with delays

In this paper, the global exponential stability for shunting inhibitory cellular neural networks (SICNNs) with delays is studied by constructing suitable Lyapunov functionals and applying some critical analysis techniques. The sufficient conditions guaranteeing the network’s global exponential stability are obtained. Our results impose less restrictive conditions than those in the references. Moreover an example is given to illustrate the feasibility of the conditions in our results.     

Global exponential stability of a class of neural networks with delays

This paper is concerned with the stability of neural networks with time-varying delays. Under assumption that the nonlinear stimulate functions are Lipschitz continuous, by means of generalized Halanay inequalities, Dini＇s derivative and functional analysis techniques, several globally exponential stability criteria are established, which are only dependent on the parameters of the system.     

Global stability and periodic solution of the viral dynamics

It is well known that the mathematical models provide very important information for the research of human immunodeficiency virus-type 1 and hepatitis C virus (HCV). However, the infection rate of almost all mathematical models is linear. The linearity shows the simple interaction between the T cells and the viral particles. In this paper, we consider the classical mathematical model with saturation response of the infection rate. By stability analysis we obtain sufficient conditions on the parameters for the global stability of the infected steady state and the infection-free steady state. We also obtain the conditions for the existence of an orbitally asymptotically stable periodic solution. Numerical simulations are presented to illustrate the results.