Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions

Authors:
Huaiqin Wu
Affiliations:
Department of Applied Mathematics, Yanshan University, Qinhuangdao 066001, China
Venue:
Information Sciences: an International Journal
Year:
2009

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Abstract

This paper investigates the global asymptotic stability of the periodic solution for a general class of neural networks whose neuron activation functions are modeled by discontinuous functions with linear growth property. By using Leray-Schauder alternative theorem, the existence of the periodic solution is proved. Based on the matrix theory and generalized Lyapunov approach, a sufficient condition which ensures the global asymptotical stability of a unique periodic solution is presented. The obtained results can be applied to check the global asymptotical stability of discontinuous neural networks with a broad range of activation functions assuming neither boundedness nor monotonicity, and also conform the validity of Forti's conjecture for discontinuous neural networks with linear growth activation functions. Two illustrative examples are given to demonstrate the effectiveness of the present results.