This paper studies the global convergence properties of Cohen-Grossberg neural networks with discrete time delays. Without assuming the symmetry of interconnection weight coefficients, and the monotonicity and differentiability of activation functions, and by employing Lyapunov functional, we derive new delay independent sufficient conditions under which a delayed Cohen-Grossberg neural network converges to a globally asymptotically stable equilibrium point. Some examples are given to illustrate the advantages of the results over the previously reported results in the literature. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Orman, Z., & Arik, S. (2006). New results for global stability of cohen-grossberg neural networks with discrete time delays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4232 LNCS, pp. 570–579). Springer Verlag. https://doi.org/10.1007/11893028_64
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