Existence of an exponential periodic attractor of a class of impulsive differential equations with time-varying delays

By using the continuation theorem due to Mawhin and Gaines, some analysis techniques and the Lyapunov functional method, the sufficient conditions ensuring the existence of an exponential periodic attractor of a class of impulsive differential equations with time-varying delays are established. The results are interesting and very different from previously known results [Xia and Wong, 2009 [7]; Tan and Tan, 2009 [19]; Huang et al., 2005 [20]; Liu and Huang, 2006 [22]]. Finally, applications and an example are given to illustrate the effectiveness of the results.     

BAM-type impulsive neural networks with time-varying delays

By using the continuation theorem of coincidence degree theory and constructing a suitable Lyapunov functional, we derive some sufficient conditions for the existence and global exponential stability of a unique periodic solution of BAM neural networks, which assumes neither the monotony nor the boundedness of the activation functions. It is believed that these results are significant and useful for the design and applications of BAM neural networks.     

Controllability analysis of linear time-varying systems with multiple time delays and impulsive effects

In this paper, the issue of controllability for linear time-varying systems with multiple time delays in the control and impulsive effects is addressed. The solution of such systems based on the variation of parameters is derived. Several sufficient and necessary algebraic conditions for two kinds of controllability, i.e., controllability to the origin and controllability, are derived. The relation among these conditions are established. A numerical example is provided to illustrate the effectiveness of the proposed methods.     

Mean square stability analysis of impulsive stochastic differential equations with delays

In this article, based on Lyapunov–Krasovskii functional method and stochastic analysis theory, we obtain some new criteria ensuring mean square stability of trivial solution of a class of impulsive stochastic differential equations with delays. As an application, a class of stochastic impulsive neural network with delays has been discussed. One illustrative example has been provided to show the effectiveness of our results.     

Dynamic analysis of Markovian jumping impulsive stochastic Cohen–Grossberg neural networks with discrete interval and distributed time-varying delays

In this paper, the dynamic analysis problem is considered for a new class of Markovian jumping impulsive stochastic Cohen–Grossberg neural networks (CGNNs) with discrete interval and distributed delays. The parameter uncertainties are assumed to be norm bounded and the discrete delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the Lyapunov–Krasovskii functional and stochastic stability theory, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities. Some asymptotic stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be easily calculated by LMI Toolbox in Matlab. A numerical example is provided to show that the proposed results significantly improve the allowable upper bounds of delays over some existing results in the literature.     

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.     

Exponential stability of impulsive high-order cellular neural networks with time-varying delays

The paper considers the problems of global exponential stability for impulsive high-order neural networks with time-varying delays. By employing the Hardy inequality and the Lyapunov functional method, we present some new criteria ensuring exponential stability. The activation functions are not assumed to be differentiable or strictly increasing, and no assumption on the symmetry of the connection matrices is necessary. These criteria are important in signal processing and the design of networks. Moreover, we also extend the previously known results. One illustrative example is also given in the end of this paper to show the effectiveness of our results.     

Stability for impulsive functional differential equations with infinite delays

In this paper, some theorems of uniform stability and uniform asymptotic stability for impulsive functional differential equations with infinite delay are proved by using Lyapunov functionals and Razumikhin techniques. An example is also proved at the end to illustrate the application of the obtained results.     

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 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.     

Existence results for an impulsive neutral functional differential equation with state-dependent delay

In this article, we study the existence of mild solutions for a class of impulsive abstract partial neutral functional differential equations with state-dependent delay. The results are obtained by using Leray–Schauder Alternative fixed point theorem.     

Global exponential stability of impulsive Cohen–Grossberg neural network with time-varying delays

In this paper, the impulsive Cohen–Grossberg neural network model with time-varying delays is considered. Applying the idea of vector Lyapunov function, M-matrix theory and inequality technique, several new sufficient conditions are obtained to ensure global exponential stability of equilibrium point for impulsive Cohen–Grossberg neural network with time-varying delays. These results generalize a few previous known results and remove some restrictions on the neural network. An example is given to show the effectiveness of the obtained results. It is believed that these results are significant and useful for the design and applications of the Cohen–Grossberg neural network.     

Boundedness results for impulsive functional differential equations with infinite delays

In this paper, boundedness criteria are established for solutions of a class of impulsive functional differential equations with infinite delays of the form $$\begin{gathered} x'(t) = F(t,x( \cdot )), t > t^ * \hfill \\ \Delta x(t_k ) = I(t_k ,x(t_k^ - )), k = 1,2,... \hfill \\ \end{gathered} $$ By using Lyapunov functions and Razumikhin technique, some new Bazumikhin-type theorems on boundedness are obtained.     

Oscillation of solutions of impulsive vector hyperbolic differential equations with delays

In this article, we investigate a class of impulsive vector hyperbolic differential equation with delays. Several sufficient conditions are established for H-oscillation of solutions of such systems subject to the Neumann boundary condition by employing certain second-order impulsive differential inequality, where H is a unit vector in ? ^M . The main results are illustrated by two examples.     

Mean square exponential stability in high-order stochastic impulsive neural networks with time-varying delays

In this paper, we consider a class of stochastic impulsive high-order neural networks with time-varying delays. By using Lyapunov functional method, LMI method and mathematics induction, some sufficient conditions are derived for the globally exponential stability of the equilibrium point of the neural networks in mean square. It is believed that these results are significant and useful for the design and applications of impulsive stochastic high-order neural networks.     

Existence of random impulsive abstract neutral non-autonomous differential inclusions with delays

This article presents the results on existence of mild solutions of random impulsive neutral functional differential inclusions under sufficient conditions. The results are obtained by using the Dhage’s fixed point theorem.