Delay dependent stability conditions of static recurrent neural networks: a non‐linear convex combination method
Feisheng Yang, Huaguang Zhang · IET Control Theory and Applications · 2014
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.