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Global exponential stability for uncertain delayed neural networks of neutral type with mixed time delays
Chang-Hua Lien1, Ker-Wei Yu, Yen-Feng Lin
1Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan, ROC. chlien@mail.nkmu.edu.tw
Abstract:
The global exponential stability for a class of uncertain delayed neural networks (DNNs) of neutral type with mixed delays is investigated in this paper. Delay-dependent and delay-independent stability criteria are proposed to guarantee the robust stability and uniqueness of equilibrium point of DNNs via linear matrix inequality and Razumikhin-like approaches. Two classes of perturbations on weighting matrices are considered in this paper. Some numerical examples are illustrated to show the effectiveness of our results.
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