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Dynamic analysis of periodic solution for high-order discrete-time Cohen-Grossberg neural networks with time delays. (English) Zbl 1327.39005

Summary: In this paper, we analyze the dynamic behavior of periodic solution for the high-order discrete-time Cohen-Grossberg neural networks (CGNNs) with time delays. First, the existence is studied based on the continuation theorem of coincidence degree theory and Young’s inequality. And then, the criterion for the global exponential stability is given using the Lyapunov method. Finally, simulation result shows the effectiveness of our proposed criterion.

MSC:

39A12 Discrete version of topics in analysis
39A23 Periodic solutions of difference equations
92B20 Neural networks for/in biological studies, artificial life and related topics
39A30 Stability theory for difference equations
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