Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

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Yang, Rongni, Zhang, Zexu and Shi, Peng (2010) Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays. IEEE Transactions on Neural Networks, 21 (1). pp. 169-175. ISSN 1045-9227

Abstract

This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy 0<d1<d(t)<d2 and is described as d(t)=d1+h(t) with 0<h(t)<d2-d1. Based on the idea of partitioning the lower bound d1, new delay-dependent stability criteria are presented by constructing a novel Lyapunov–Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/7447
DOI https://doi.org/10.1109/TNN.2009.2036610
Official URL http://dx.doi.org/10.1109/TNN.2009.2036610
Subjects Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
Current > FOR Classification > 0802 Computation Theory and Mathematics
Historical > SEO Classification > 970108 Expanding Knowledge in the Information and Computing Sciences
Keywords ResPubID19944, delay partitioning, exponential stability, interval time-varying delay, stochastic neural networks (SNNs)
Citations in Scopus 182 - View on Scopus
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