For a family of differential equations with infinite delay, we give sufficient conditions for the global asymptotic, and global exponential stability of an equilibrium point. This family includes most of the delayed models of neural networks of Cohen-Grossberg type, with both bounded and unbounded distributed delay, for which general asymptotic and exponential stability criteria are derived. As...

In this paper, the exponential stability of periodic solutions for inertial Cohen-Grossberg-type neural networks are investigated. First, by properly chosen variable substitution the system is transformed to first order differential equation. Second, some sufficient conditions which can ensure the existence and exponential stability of periodic solutions for the system are obtained by using con...

Impulsive Cohen-Grossberg neural networks with bounded and unbounded delays i.e., mixed delays are investigated. By using the Leray-Schauder fixed point theorem, differential inequality techniques, and constructing suitable Lyapunov functional, several new sufficient conditions on the existence and global exponential stability of periodic solution for the system are obtained, which improves som...

Abstract: The paper is concerned with the existence and global exponential stability of periodic solutions for inertial Cohen-Grossberg-type BAM neural networks with time delays. With variable transformation the system is transformed to first order differential equations. Some new sufficient conditions ensuring the existence and global exponential stability of periodic solutions for the system ...

ABSTRACT. In this paper, the robust exponential stability problem is investigated for a class of uncertain Markovian jumping stochastic Cohen-Grossberg type bidirectional associative memory neural networks (CGBAMNN) with time-varying delays and reaction-diffusion terms. By using the Lyapunov stability theory and linear matrix inequality (LMI) technique, some robust stability conditions guarante...

Cohen and Grossberg (1983) studied an almost gradient system of ordinary differential equations with application to neural networks and used a Liapunov function, together with an invariance principle, to show that some equilibrium points attract solutions. Independently, Hopfield (1984) modeled a neural network by means of a system of ordinary differential equations which turn out to be a speci...

The existence and global asymptotic stability of a large class of Cohen-Grossberg neural networks is discussed in this paper. Previous papers always assume that the amplification function has positive lower and upper bounds, which excludes a large class of functions. In our paper, it is only needed that the amplification function is positive. Also, the model discussed is general, the method use...

We establish an analytic local Hopf bifurcation theorem and a topological global Hopf bifurcation theorem to detect the existence and to describe the spatial-temporal pattern, the asymptotic form and the global continuation of bifurcations of periodic wave solutions for functional differential equations in the presence of symmetry. We apply these general results to obtain the coexistence of mul...