Anti-periodic solutions of Cohen-Grossberg shunting inhibitory cellular neural networks on time scales

Volume 9, Issue 5, pp 2376--2388 http://dx.doi.org/10.22436/jnsa.009.05.38

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Authors

Changjin Xu - Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang 550004, P. R. China. Yicheng Pang - School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550004, P. R. China. Peiluan Li - School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, P. R. China.

Abstract

In this paper, Cohen-Grossberg shunting inhibitory cellular neural networks(CGSICNNs) on time scales are investigated. Some sufficient conditions which ensure the existence and global exponential stability of anti-periodic solutions for a class of CGSICNNs on time scales are established. Numerical simulations are carried out to illustrate the theoretical findings. The results obtained in this paper are of great significance in designs and applications of globally stable anti-periodic Cohen-Grossberg shunting inhibitory cellular neural networks.

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Keywords

• Cohen-Grossberg shunting inhibitory cellular neural networks
• anti-periodic solution
• exponential stability
• time scales.

MSC

•  34C25
•  34K13
•  34K25

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