In this paper, we discuss delayed Cohen-Grossberg neural networks with time-varying and distributed delays and investigate their global asymptotical stability of the equilibrium point. The model proposed in this paper is universal. A set of sufficient conditions ensuring global convergence and globally exponential convergence for the Cohen-Grossberg neural networks with time-varying and distributed delays are given. Most of the existing models and global stability results for Cohen-Grossberg neural networks, Hopfield neural networks and cellular neural networks can be obtained from the theorems given in this paper. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Chen, T., & Lu, W. (2006). Global asymptotical stability of Cohen-Grossberg neural networks with time-varying and distributed delays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3971 LNCS, pp. 192–197). Springer Verlag. https://doi.org/10.1007/11759966_29
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