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Stability Analysis for Neural Networks With Time-Varying Delay Based on Quadratic Convex Combination

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4 Author(s)
Huaguang Zhang ; Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China ; Feisheng Yang ; Xiaodong Liu ; Qingling Zhang

In this paper, a novel method is developed for the stability problem of a class of neural networks with time-varying delay. New delay-dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time-varying delay are derived by the newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results by using the first-order convex combination property, our derivation applies the idea of second-order convex combination and the property of quadratic convex function which is given in the form of a lemma without resorting to Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:24 ,  Issue: 4 )