New stability criteria for BAM neural networks with time-varying delays

Authors:
Liang Hu;Hao Liu;Yingbo Zhao
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China;Department of Automation Measurement and Control, School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China
Venue:
Neurocomputing
Year:
2009

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Abstract

In this paper, the exponential stability is investigated for a class of time-delay BAM neural networks (NNs). Time delays of two layers are taken into account separately rather than as a whole with the idea of delay fractioning. Then we generalize the result to time-varying interval delay condition. Exploiting the known constant part of delay sufficiently to estimate the upper bounds, we can derive an improved stability for BAM NNs with time-varying interval delay. Two examples are provided to demonstrate the less conservatism and effectiveness of the proposed linear matrix inequality (LMI) conditions.