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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2013

01.12.2013

Delay-Dependent Robust Exponential Stability of Impulsive Markovian Jumping Reaction-Diffusion Cohen-Grossberg Neural Networks

verfasst von: Yonggui Kao, Changhong Wang, Lin Zhang

Erschienen in: Neural Processing Letters | Ausgabe 3/2013

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Abstract

This paper is devoted to investigating delay-dependent robust exponential stability for a class of Markovian jump impulsive stochastic reaction-diffusion Cohen-Grossberg neural networks (IRDCGNNs) with mixed time delays and uncertainties. The jumping parameters, determined by a continuous-time, discrete-state Markov chain, are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing a Lyapunov–Krasovskii functional, and using poincarè inequality and the mathematical induction method, several novel sufficient criteria ensuring the delay-dependent exponential stability of IRDCGNNs with Markovian jumping parameters are established. Our results include reaction-diffusion effects. Finally, a Numerical example is provided to show the efficiency of the proposed results.
Nächster Artikel Delay-Dependent Exponential Stability of Cellular Neural Networks with Multi-Proportional Delays

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Metadaten
Titel
Delay-Dependent Robust Exponential Stability of Impulsive Markovian Jumping Reaction-Diffusion Cohen-Grossberg Neural Networks
verfasst von
Yonggui Kao
Changhong Wang
Lin Zhang
Publikationsdatum
01.12.2013
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2013
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-012-9269-2

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