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New Stability Criteria for Stochastic Delayed Neural Networks of Neutral-Type with Uncertainties and Time-Varying Delays

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Abstract:

In this paper, the problem of delay-dependent robust stability analysis is investigated for delayed neural networks of neutral-type with parameter uncertainties and stochastic perturbations. Based on an appropriate LyapunovKrasovskii functional, the stochastic stability theory and the free-weighting matrix method, novel delay-dependent robust stability criteria are derived in terms of linear matrix inequality (LMI). Three examples are given to illustrate the feasibility and effectiveness of the proposed results.

Keywords: DELAY-DEPENDENT ROBUST STABILITY; LINEAR MATRIX INEQUALITY; LYAPUNOV-KRASOVSKII FUNCTIONAL; NEUTRAL-TYPE; STOCHASTIC DELAYED NEURAL NETWORKS

Document Type: Research Article

DOI: http://dx.doi.org/10.1166/asl.2012.2233

Publication date: March 1, 2012

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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