Delay-dependent stability for uncertain stochastic neural networks with time-varying delay

He Huang and Gang Feng

Physica A: Statistical Mechanics and its Applications, 2007, vol. 381, issue C, 93-103

Abstract: This paper is concerned with the robust stability analysis problem for uncertain stochastic neural networks with time-varying delay. The parameter uncertainties are assumed to be norm bounded. By defining a new Lyapunov–Krasovskii functional, the restrictions such as the time-varying delay was required to be differentiable and its derivative was strictly smaller than one, are removed. Based on the linear matrix inequality approach, delay-dependent stability criteria are obtained such that for all admissible uncertainties, the stochastic neural network is globally asymptotically stable in the mean square. Two slack variables are introduced into the obtained stability criteria to reduce the conservatism. Finally, a numerical example is given to illustrate the effectiveness of the developed method.

Keywords: Recurrent neural networks; Stochastic systems; Uncertain systems; Time-varying delay; Robust stability (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:381:y:2007:i:c:p:93-103

DOI: 10.1016/j.physa.2007.04.020

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