New delay dependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays

This paper is concerned with the stability analysis problem for a class of delayed stochastic recurrent neural networks with both discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establi...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2012-08, Vol.349 (6), p.2108-2123
Main Authors: Raja, R., Samidurai, R.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Asymptotic properties
Computer science
control theory
systems
Connectionism. Neural networks
Control system analysis
Control theory. Systems
Exact sciences and technology
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Summary:This paper is concerned with the stability analysis problem for a class of delayed stochastic recurrent neural networks with both discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to ensure the global, robust asymptotic stability for the addressed system in the mean square. The conditions obtained here are expressed in terms of LMIs whose feasibility can be checked easily by MATLAB LMI Control toolbox. In addition, two numerical examples with comparative results are given to justify the obtained stability results.
ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2012.03.007