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

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 impuls...

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Bibliographic Details
Published in:Neural networks 2006-12, Vol.19 (10), p.1581-1590
Main Authors: Ho, Daniel W.C., Liang, Jinling, Lam, James
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Bidirectional associative memory neural networks
Computer science
control theory
systems
Connectionism. Neural networks
Delay
Differential inequality
Exact sciences and technology
Global exponential stability
Humans
Impulse
Linear matrix inequality
Linear Models
Memory - physiology
Nerve Net
Neural Networks (Computer)
Numerical Analysis, Computer-Assisted
Time Factors
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2006.02.006