Exponential stability of competitive neural networks with time-varying and distributed delays

In this paper, time-varying and distributed delays are introduced into competitive neural networks and the global exponential stability for the neural networks is investigated. By using this analysis method, inequality techniques, and the properties of an M-matrix, several novel delay-independent sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium point are derived. The obtained results are different from and less restrictive than those given in the existing literature, and the boundedness and differentiability of the activation functions and the differentiability of the time-varying delays are removed. Two examples with their simulations are given to show their good agreement with the theoretical results.

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