Mean Square Exponential Stability of Uncertain Stochastic Hopfield Neural Networks with Interval Time-Varying Delays

Authors:
Jiqing Qiu;Hongjiu Yang;Yuanqing Xia;Jinhui Zhang
Affiliations:
College of Sciences, Hebei University of Science and Technology, Shijiazhuang, 050018, China;College of Sciences, Hebei University of Science and Technology, Shijiazhuang, 050018, China;Department of Automatic Control, Beijing Institute of Technology, Beijing 100081, China;Department of Automatic Control, Beijing Institute of Technology, Beijing 100081, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

The problem of mean square exponential stability of uncertain stochastic Hopfield neural networks with interval time-varying delays is investigated in this paper. The delay factor is assumed to be time-varying and belongs to a given interval, which means that the derivative of the delay function can exceed one. The uncertainties considered in this paper are norm-bounded and possibly time-varying. By Lyapunov-Krasovskii functional approach and stochastic analysis approach, a new delay-dependent stability criteria for the exponential stability of stochastic Hopfield neural networks is derived in terms of linear matrix inequalities(LMIs). A simulation example is given to demonstrate the effectiveness of the developed techniques.