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LMI Conditions for Robust Stability Analysis of Stochastic Hopfield Neural Networks with Interval Time-Varying Delays and Linear Fractional Uncertainties

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Abstract

In this paper, the delay-dependent robust stability for a class of stochastic neural networks with linear fractional uncertainties is studied. The time-varying delay is assumed to belong to an interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the Lyapunov–Krasovskii functional, stochastic stability theory and some inequality techniques, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). In order to derive less conservative results, few free-weighting matrices are introduced. Three numerical examples are presented to show the effectiveness and improvement of the proposed method.

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Authors and Affiliations

  1. Department of Mathematics, Gandhigram Rural University, Gandhigram, 624 302, Tamilnadu, India

    P. Balasubramaniam & S. Lakshmanan

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  1. P. Balasubramaniam
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  2. S. Lakshmanan
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Corresponding author

Correspondence to P. Balasubramaniam.

Additional information

The work of authors was supported by Department of Science and Technology, New Delhi, India, under the sanctioned No. SR/S4/MS:485/07.

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Balasubramaniam, P., Lakshmanan, S. LMI Conditions for Robust Stability Analysis of Stochastic Hopfield Neural Networks with Interval Time-Varying Delays and Linear Fractional Uncertainties. Circuits Syst Signal Process 30, 1011–1028 (2011). https://doi.org/10.1007/s00034-010-9260-y

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  • DOI: https://doi.org/10.1007/s00034-010-9260-y

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