Global robust stability of delayed neural networks with a class of general activation functions

Authors:
He Huang;Jinde Cao;Yuzhong Qu
Affiliations:
Department of Computer Science and Engineering Southeast University, Nanjing 210096, PR China;Department of Mathematics, Southeast University, Nanjing 210096, PR China;Department of Computer Science and Engineering Southeast University, Nanjing 210096, PR China
Venue:
Journal of Computer and System Sciences
Year:
2004

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Abstract

In this paper, the global robust stability is discussed for delayed neural networks with a class of general activation functions. By constructing new Lyapunov functionals, several novel conditions are derived to guarantee the existence, uniqueness and global robust stability of the equilibrium of neural networks with time delays. These conditions do not require the activation functions to be differentiable, bounded or monotonically nondecreasing. The results obtained here are generalizations of some earlier results reported in the literature for neural networks with time delays. In addition, two examples are given to illustrate our proposed results.