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On the Robust Stability Problem of a Class of Stochastic BAM Neural Networks with Both Neutral-Type Delays and Uncertainties

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Abstract:

In this paper, a class of stochastic BAM neural networks with neutral-type delays and parameter uncertainties is considered. The neutral-type delays are assumed to be time-varying and the parameter uncertainties are assumed to be norm bounded. New global robust stability criteria are derived by constructing new LyapunovKrasovskii functional and combing the method of inequality analysis. These criteria are expressed in the form of linear matrix inequality (LMI) and they can easily be checked. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed results.

Keywords: BAM NEURAL NETWORKS; LINEAR MATRIX INEQUALITY; LYAPUNOV-KRASOVISKII FUNCTIONAL; NEUTRAL-TYPE DELAY; ROBUST STABILITY; UNCERTAINTY

Document Type: Research Article

DOI: http://dx.doi.org/10.1166/asl.2012.2225

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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