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

By DWC Ho, J Lam and J Liang


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. © 2006 Elsevier Ltd. All rights reserved.link_to_subscribed_fulltex

Topics: Linear Models, Algorithms, Humans, Memory - Physiology, Nerve Net, Neural Networks (Computer), Numerical Analysis, Computer-Assisted, Time Factors
Publisher: 'Elsevier BV'
Year: 2006
DOI identifier: 10.1016/j.neunet.2006.02.006
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Provided by: HKU Scholars Hub
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