New stability criteria for neutral-type Cohen–Grossberg neural networks with discrete and distributed delays

Abstract

This paper studies the existence, uniqueness and globally robust exponential stability for a class of uncertain neutral-type Cohen–Grossberg neural networks with time-varying and unbounded distributed delays. Based on Lyapunov–Krasovskii functional, by involving a free-weighting matrix, using the homeomorphism mapping principle, Cauchy–Schwarz inequality, Jensen integral inequality, linear matrix inequality techniques and matrix decomposition method, several delay-dependent and delay-independent sufficient conditions are obtained for the robust exponential stability of considered neural networks. Two numerical examples are given to show the effectiveness of our results.

Keywords:

• global robust stability
• neutral-type
• Jensen integral inequality
• free-weighting matrix
• Cohen–Grossberg neural networks
• homeomorphism mapping

2000 AMS Subject Classifications :

• 93D05
• 92B20

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 60774093, by the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 200801451096, by the China Postdoctoral Science Foundation under Grants 20080431150 and 200902547, the National High Technology Research and Development Program under Grant 2009AA04Z127 and Program for New Century Excellent Talents in University under Grant NCET-08-0101.