A new robust stability criterion for dynamical neural networks with multiple time delays

Authors:
Ozlem Faydasicok;Sabri Arik
Affiliations:
Istanbul University, Department of Mathematics, 34134 Vezneciler, Istanbul, Turkey;Isik University, Department of Electrical and Electronics Engineering, 34980 Sile, Istanbul, Turkey
Venue:
Neurocomputing
Year:
2013

Citing 23
Cited 6

Topics in matrix analysis

Topics in matrix analysis
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Abstract

This paper investigates the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we derive a new criterion for the robust stability of a class of delayed neural networks by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Different from those previously published conditions in the recent literature, the robust stability result presented in this paper not only establishes a time-independent relationship between the network parameters of the neural network, but also takes into account the number the neurons of the designed neural system. Some illustrative numerical examples are also given to make a detailed comparison between our result and the previously published corresponding results. This comparison proves that our result is new and can be considered an alternative condition to those of the previously reported robust stability results.