Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
404907 | Neural Networks | 2006 | 10 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Daniel W.C. Ho, Jinling Liang, James Lam,