Stability and dissipativity analysis of static neural networks with interval time-varying delay

This paper focuses on the problems of stability and dissipativity analysis for static neural networks (NNs) with interval time-varying delay. A new augmented Lyapunov–Krasovskii functional is firstly constructed, in which the information on the activation function is taken fully into account. Then,...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2015-03, Vol.352 (3), p.1284-1295
Main Authors: Zeng, Hong-Bing, Park, Ju H., Zhang, Chang-Fan, Wang, Wei
Format: Article
Language:English
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Summary:This paper focuses on the problems of stability and dissipativity analysis for static neural networks (NNs) with interval time-varying delay. A new augmented Lyapunov–Krasovskii functional is firstly constructed, in which the information on the activation function is taken fully into account. Then, by employing a Wirtinger-based inequality to estimate the derivative of Lyapunov–Krasovskii functional, an improved stability criterion is derived for the considered neural networks. The result is extended to dissipativity analysis and a sufficient condition is established to assure the neural networks strictly dissipative. Two numerical examples are provided to demonstrate the effectiveness and the advantages of the proposed method.
ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2014.12.023