© The Institution of Engineering and Technology
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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