Abstract
In this paper, we study the global exponential stability of anti-periodic solution of Cohen-Grossberg neural networks with mixed delays and distributed delays. Based on Lyapunov function and contraction mapping theorem, we introduce some sufficient conditions to ensure the existence and exponential stability of anti-periodic solution of Cohen-Grossberg neural networks. Finally, some numerical examples are provided to show the effectiveness of the obtained results.
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CITATION STYLE
Qin, S., Tan, Y., & Wang, F. (2016). Exponential stability of anti-periodic solution of Cohen-Grossberg neural networks with mixed delays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9719, pp. 160–167). Springer Verlag. https://doi.org/10.1007/978-3-319-40663-3_19
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