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