New results for global exponential stability of delayed cohen-grossberg neural networks

Authors:
Anhua Wan;Hong Qiao;Bo Zhang;Weihua Mao
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Applied Mathematics, Chinese Academy of Sciences, Beijing, China;College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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Abstract

The exponential stability is discussed for Cohen-Grossberg neural networks with discrete delays. Without assuming the boundedness, differentiability and monotonicity of the activation functions, the nonlinear measure approach is employed to analyze the existence and uniqueness of an equilibrium, and a novel Lyapunov functional is constructed to investigate the exponential stability of the networks. New general sufficient conditions, which are independent of the delays, are derived for the global exponential stability of the delayed neural networks.