Global Asymptotic Stability of Cohen-Grossberg Neural Networks with Multiple Discrete Delays

Authors:
Anhua Wan;Weihua Mao;Hong Qiao;Bo Zhang
Affiliations:
School of Mathematics and Computational Science, Sun Yat-Sen University, 510275 Guangzhou, China and Institute of Automation, Chinese Academy of Sciences, 100080 Beijing, China;Department of Applied Mathematics, College of Science, South China Agricultural University, 510642 Guangzhou, China and College of Automation Science and Engineering, South China University of Tec ...;Institute of Automation, Chinese Academy of Sciences, 100080 Beijing, China;Institute of Applied Mathematics, Chinese Academy of Sciences, 100080 Beijing, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

The asymptotic stability is analyzed for Cohen-Grossberg neural networks with multiple discrete delays. The boundedness, differentiability or monotonicity condition is not assumed on the activation functions. The generalized Dahlquist constant approach is employed to examine the existence and uniqueness of equilibrium of the neural networks, and a novel Lyapunov functional is constructed to investigate the stability of the delayed neural networks. New general sufficient conditions are derived for the global asymptotic stability of the neural networks with multiple delays.