Stochastic stability analysis for delayed neural networks of neutral type with Markovian jump parameters

Xuyang Lou and Baotong Cui

Chaos, Solitons & Fractals, 2009, vol. 39, issue 5, 2188-2197

Abstract: In this paper, the problem of stochastic stability for a class of delayed neural networks of neutral type with Markovian jump parameters is investigated. The jumping parameters are modelled as a continuous-time, discrete-state Markov process. A sufficient condition guaranteeing the stochastic stability of the equilibrium point is derived for the Markovian jumping delayed neural networks (MJDNNs) with neutral type. The stability criterion not only eliminates the differences between excitatory and inhibitory effects on the neural networks, but also can be conveniently checked. The sufficient condition obtained can be essentially solved in terms of linear matrix inequality. A numerical example is given to show the effectiveness of the obtained results.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:39:y:2009:i:5:p:2188-2197

DOI: 10.1016/j.chaos.2007.06.114

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