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Global stability in switched recurrent neural networks with time-varying delay via nonlinear measure. (English) Zbl 1176.92003
Summary: Based on switched systems and recurrent neural networks (RNNs) with time-varying delays, a model of switched RNNs is formulated. Global asymptotical stability (GAS) and global robust stability (GRS) for such switched neural networks are studied by employing nonlinear measure and linear matrix inequality (LMI) techniques. Some new sufficient conditions are obtained to ensure GAS or GRS of the unique equilibrium of the proposed switched system. Furthermore, the proposed LMI results are computationally efficient as they can be solved numerically with standard commercial software. Finally, three examples are provided to illustrate the usefulness of the results.

MSC:
92B20 Neural networks for/in biological studies, artificial life and related topics
34K20 Stability theory of functional-differential equations
92-08 Computational methods for problems pertaining to biology
34K25 Asymptotic theory of functional-differential equations
68T05 Learning and adaptive systems in artificial intelligence
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