ABSTRACT. We study impulsive Cohen-Grossberg neural networks with S-type distributed delays. This type of delays in the presence of impulses is more general than the usual types of delays studied in the literature. Using analysis techniques we prove the existence of a unique equilibrium point. By means of simple and efficient Lyapunov functions we present some sufficient conditions for the expo...

In this paper, a generalized model of Cohen-Grossberg neural networks (CGNNs) with time-varying delays and reaction-diffusion term is investigated. By constructing suitable Lyapunov functional, inequality technique and M -matrix theory, some sufficient conditions for global exponential stability of generalized CGNNs with time-varying delays and reaction-diffusion term are obtained. An examples ...

In this paper, the exponential stability problems are addressed for a class of delayed Cohen-Grossberg neural networks which are also perturbed by some stochastic noises. By employing the Lyapunov method, stochastic analysis and some inequality techniques, sufficient conditions are acquired for checking the pth(p > 1) and the 1st moment exponential stability of the network. Finally, One example...

In this paper, the problem of dynamics analysis for a class of new impulsive stochastic Cohen–Grossberg neural networks with Markovian jumping and mixed time delays is researched. Some criteria for the asymptotical stability in mean square are obtained based on linear matrix inequality (LMI) forms, which can be easily solved by LMI Toolbox in Matlab. An example is given to show the effectivenes...

In this paper, the global asymptotic stability is studied for a class of Cohen-Grossberg neural networks with time-varying and distributed delays. By employing nonlinear measure and linear matrix inequality (LMI) techniques, some new sufficient conditions are obtained to ensure the existence, uniqueness of the equilibrium point and its stability for CGNNs, where the activation functions need on...

Abstract: In this paper, the mean square exponential stability of the periodic solution for stochastic reactiondiffusion high-order Cohen-Grossberg-Type BAM neural networks with time delays is investigated. By constructing suitable Lyapunov function, applying Itô formula and Poincaré mapping, we give some sufficient conditions to guarantee the mean square exponential stability of the periodic s...

Chartier and his colleagues have recently proposed a nonlinear synchronous attractor neural network. In the Nonlinear Dynamic Recurrent Associative Memory (NDRAM), learning has been shown to converge to a set of real-valued attractors in single-layered neural networks and bidirectional associative memories. However, the transmission is highly nonlinear and its global stability has never been an...

Abstract In this article, we investigate exponential lag synchronization results for the Cohen–Grossberg neural networks with discrete and distributed delays on an arbitrary time domain by applying feedback control. We formulate problem using scales theory so that can be applied to any uniform or non-uniform domains. Also, provide a comparison of shows obtained are unified generalize existing r...