Global exponential stability for uncertain delayed neural networks of neutral type with mixed time delays.
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.
Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan, ROC. chlien@mail.nkmu.edu.tw
Chang-Hua
CH
Lien
Ker-Wei
KW
Yu
Yen-Feng
YF
Lin
Yeong-Jay
YJ
Chung
Long-Yeu
LY
Chung
D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors