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Delay dependent stability conditions of static recurrent neural networks: a non-linear convex combination method

Delay dependent stability conditions of static recurrent neural networks: a non-linear convex combination method

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A new method is developed for stability of static recurrent neural networks with time-varying delay in this study. Improved delay-dependent conditions in the form of a set of linear matrix inequalities are derived for this class of static nets through the newly proposed augmented Lyapunov–Krasovski functional. Our derivation employs a novel non-linear convex combination technique, that is, quadratic convex combination. Different from previous results, the property of quadratic convex function is fully taken advantage of without resort to the Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.

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http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2014.0117
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