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Stability and dissipativity analysis of static neural networks with interval time-varying delay. (English) Zbl 1307.93446
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.

MSC:
93E15 Stochastic stability in control theory
92B20 Neural networks for/in biological studies, artificial life and related topics
93D30 Lyapunov and storage functions
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