Stability analysis of Cohen-Grossberg neural networks

IEEE Transactions on Neural Networks
Shangjiang Guo, Lihong Huang

Abstract

Without assuming boundedness and differentiability of the activation functions and any symmetry of interconnections, we employ Lyapunov functions to establish some sufficient conditions ensuring existence, uniqueness, global asymptotic stability, and even global exponential stability of equilibria for the Cohen-Grossberg neural networks with and without delays. Our results are not only presented in terms of system parameters and can be easily verified and also less restrictive than previously known criteria and can be applied to neural networks, including Hopfield neural networks, bidirectional association memory neural networks, and cellular neural networks.

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Citations

Oct 19, 2006·International Journal of Neural Systems·K Gopalsamy
Jan 22, 2010·IEEE Transactions on Neural Networks·Wu-Hua Chen, Wei Xing Zheng
Nov 20, 2009·IEEE Transactions on Neural Networks·Zhanshan Wang, Huaguang Zhang
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Nov 8, 2008·IEEE Transactions on Neural Networks·Huaguang ZhangDerong Liu
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