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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:
92B20General theory of neural networks (mathematical biology)
34K20Stability theory of functional-differential equations
92-08Computational methods (appl. to natural sciences)
34K25Asymptotic theory of functional-differential equations
68T05Learning and adaptive systems
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