New delay-dependent exponential stability criteria of BAM neural networks with time delays

Authors:
Degang Yang;Xiaofeng Liao;Chunyan Hu;Yong Wang
Affiliations:
College of Mathematics and Computer Science, Chongqing Normal University, Chongqing 400047, China and College of Computer Science and Engineering, Chongqing University, Chongqing 400044, China;College of Computer Science and Engineering, Chongqing University, Chongqing 400044, China;College of Mathematics and Computer Science, Chongqing Normal University, Chongqing 400047, China;College of Computer Science, Chongqing University of Posts and Telecommunication, Chongqing 400065, China and College of Computer Science and Engineering, Chongqing University, Chongqing 400044, C ...
Venue:
Mathematics and Computers in Simulation
Year:
2009

Citing 14
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Abstract

In this paper, the global exponential stability is investigated for the bi-directional associative memory networks with time delays. Several new sufficient conditions are presented to ensure global exponential stability of delayed bi-directional associative memory neural networks based on the Lyapunov functional method as well as linear matrix inequality technique. To the best of our knowledge, few reports about such ''linearization'' approach to exponential stability analysis for delayed neural network models have been presented in literature. The method, called parameterized first-order model transformation, is used to transform neural networks. The obtained conditions show to be less conservative and restrictive than that reported in the literature. Two numerical simulations are also given to illustrate the efficiency of our result.