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    Global exponential stability criteria for neural networks with probabilistic delays

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    Mahmoud, Magdi S, Selim, S and Shi, Peng (2010) Global exponential stability criteria for neural networks with probabilistic delays. IET Control Theory and Applications, 4 (11). pp. 2405-2415. ISSN 1751-8644

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    Abstract

    The problem of global exponential stability analysis for a class of neural networks (NNs) with probabilistic delays is discussed in this paper. The delay is assumed to follow a given probability density function. This function is discretised into arbitrary number of intervals. In this way, the NN with random time delays is transformed into one with deterministic delays and random parameters. New conditions for the exponential stability of such NNs are obtained by employing new Lyapunov–Krasovskii functionals and novel techniques for achieving delay dependence. It is established that these conditions reduce the conservatism by considering not only the range of the time delays, but also the probability distribution of their variation. Numerical examples are provided to show the advantages of the proposed techniques.

    Item Type: Article
    Uncontrolled Keywords: ResPubID20195, Lyapunov-Krasovskii functional, deterministic delay, global exponential stability, neural network, probabilistic delay, probability density function, probability distribution, random parameter
    Subjects: Current > FOR Classification > 0802 Computation Theory and Mathematics
    Current > FOR Classification > 0902 Automotive Engineering
    Historical > SEO Classification > 970108 Expanding Knowledge in the Information and Computing Sciences
    Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
    Depositing User: VUIR
    Date Deposited: 20 Dec 2011 22:27
    Last Modified: 24 Mar 2015 04:10
    URI: http://vuir.vu.edu.au/id/eprint/7216
    DOI: https://doi.org/10.1049/iet-cta.2009.0007
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    Citations in Scopus: 23 - View on Scopus

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