Journal
NEURAL NETWORKS
Volume 19, Issue 10, Pages 1581-1590Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2006.02.006
Keywords
bidirectional associative memory neural networks; delay; differential inequality; global exponential stability; impulse; linear matrix inequality
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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. (c) 2006 Elsevier Ltd. All rights reserved.
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