New criteria for globally exponential stability of delayed Cohen‐Grossberg neural network

New criteria for globally exponential stability of delayed Cohen‐Grossberg neural network

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Article ID: iaor201527004
Volume: 79
Issue: 5
Start Page Number: 1527
End Page Number: 1543
Publication Date: Jan 2009
Journal: Mathematics and Computers in Simulation
Authors: , ,
Keywords: stability
Abstract:

This paper is concerned with analysis problem for the global exponential stability of the Cohen–Grossberg neural networks with discrete delays and with distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, we employ Lyapunov functions to establish some sufficient conditions ensuring global exponential stability of equilibria for the Cohen–Grossberg neural networks with discrete delays and with distributed 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. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed neural networks.

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