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Impulses-induced exponential stability in recurrent delayed neural networks

Impulses-induced exponential stability in recurrent delayed neural networks,10.1016/j.neucom.2011.05.001,Neurocomputing,Quanjun Wu,Jin Zhou,Lan Xiang

Impulses-induced exponential stability in recurrent delayed neural networks   (Citations: 1)
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The present paper formulates and studies a model of recurrent neural networks with time-varying delays in the presence of impulsive connectivity among the neurons. This model can well describe practical architectures of more realistic neural networks. Some novel yet generic criteria for global exponential stability of such neural networks are derived by establishing an extended Halanay differential inequality on impulsive delayed dynamical systems. The distinctive feature of this work is to address exponential stability issues without a priori stability assumption for the corresponding delayed neural networks without impulses. It is shown that the impulses in neuronal connectivity play an important role in inducing global exponential stability of recurrent delayed neural networks even if it may be unstable or chaotic itself. Furthermore, example and simulation are given to illustrate the practical nature of the novel results.
Journal: Neurocomputing - IJON , vol. 74, no. 17, pp. 3204-3211, 2011
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    • ...Recently, Lemma 1 has widely been applied to analyze impulsive effects on stability of nonlinear delayed dynamical systems in many practical applications, such as designs and applications of neural networks, synchronization of coupled oscillators, and consensus problems of networked control systems, among many others [28], [29], [35]...

    Jin Zhouet al. Pinning Complex Delayed Dynamical Networks by a Single Impulsive Contr...

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