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An Linear Matrix Inequality Approach to the Global Asymptotic Stability for DelayedCellular Neural Networks

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The stability of delayed cellular neural networks is considered. By combining the Lyapunov functional with the linear matrix inequality (LMI) technique, several global asymptotic stability conditions are derived. The method is simple and straightforward. The results obtained in this paper generalize some earlier results in the literature. An example is illustrated to show the applicability of the results.

Keywords: DELAYED CELLULAR NEURAL NETWORKS; GLOBAL ASYMPTOTIC STABILITY; LINEAR MATRIX INEQUALITY; LYAPUNOV FUNCTIONAL

Document Type: Research Article

Publication date: 01 November 2007

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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