Improved exponential stability criteria for neural networks with time-varying delays

Authors:
Junkang Tian;Shouming Zhong;Yong Wang
Affiliations:
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China and Key Laboratory for Neuroinformation of Ministry of Education, Universi ...;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China
Venue:
Neurocomputing
Year:
2012

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Abstract

This paper concerned the problem of exponential stability criteria for neural networks with discrete and distributed time-varying delays. By dividing the delay interval into multiple segments and choosing a new Lyapunov functional, some improved stability criteria are derived in terms of linear matrix inequalities. The obtained criteria are less conservative due to a convex optimization approach is considered. Finally, two numerical examples are given to illustrate the effectiveness of the proposed method.