Global exponential stability of impulsive high-order BAM neural networks with time-varying delays

Authors:
Daniel W. C. Ho;Jinling Liang;James Lam
Affiliations:
Department of Mathematics, City University of Hong Kong, 83 Tat Chee Ave., Hong Kong;Department of Mathematics, Southeast University, Nanjing 210096, China and Department of Mathematics, City University of Hong Kong, 83 Tat Chee Ave., Hong Kong;Department of Mechanical Engineering, University of Hong Kong, Hong Kong
Venue:
Neural Networks
Year:
2006

Citing 12
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Abstract

In this paper, global exponential stability and exponential convergence are studied for a class of impulsive high-order bidirectional associative memory (BAM) neural networks with time-varying delays. By employing linear matrix inequalities (LMIs) and differential inequalities with delays and impulses, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Three illustrative examples are also given at the end of this paper to show the effectiveness of our results.