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In this paper, the exponential stability of Cohen-Grossberg neural networks with distributed delays is investigated. Using the M-matrix property, the necessary and sufficient condition for the existence, uniqueness and exponential stability of the equilibrium point of the neural networks is obtained. We avoid the difficulty of proving the existence of a positive invariant set as in some previous works.

In this paper, the global robust dissipativity of integro-differential systems modeling neural networks with time-varying delays are studied. Proper Lyapunov functionals and some analytic techniques are employed to derive the sufficient conditions under which the networks proposed are the global robust dissipativity. The results are shown to improve the previous global dissipativity results derived in the literature. Some examples are given to illustrate the correctness of our results.

Some sufficient conditions for the ultimate boundedness and global exponential stability of a class of nonautonomous reaction–diffusion cellular neural networks (RCNNs) with distributed delays are obtained by means of the Lyapunov functional method. Without assuming that the activation functions fijl(⋅) are bounded, the results extend and improve the earlier publications.

This paper discusses a generalized model of high-order Hopfield-type neural networks with time-varying delays. Some novel global stability criteria of the system is derived by using Lyapunov method, linear matrix inequality (LMI) and analytic technique. The LMI-based criteria obtained here are computationally more flexible and more generic than many other existing criteria. A numerical example is given to illustrate our result.

This letter considers the problem of asymptotic stability of Cohen-Grossberg neural networks with time-varying delays. The stability condition is given in terms of a linear matrix inequality (LMI). Comparison between our results and previous results admits that our results establish a new set of stability criteria for delayed Cohen-Grossberg neural networks.

The global asymptotic stability of delay bi-directional associative memory neural networks with impulses are studied by constructing suitable Lyapunov functional. Sufficient conditions, which are independent to the delayed quantity, are obtained for the global asymptotic stability of the neural networks. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.

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.