Title :
Global Exponential Stability for Uncertain Delayed Neural Networks of Neutral Type With Mixed Time Delays
Author :
Lien, Chang-Hua ; Yu, Ker-Wei ; Lin, Yen-Feng ; Chung, Yeong-Jay ; Chung, Long-Yeu
Author_Institution :
Dept. of Marine Eng., Nat. Kaohsiung Marine Univ., Kaohsiung
fDate :
6/1/2008 12:00:00 AM
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.
Keywords :
asymptotic stability; delay systems; linear matrix inequalities; neural nets; stability criteria; delay-independent stability criteria; global exponential stability; linear matrix inequality; mixed time delays; uncertain delayed neural networks; Delay-dependent criterion; delay-independent criterion; delayed neural networks (DNNs) of neutral type; global exponential stability; Algorithms; Computer Simulation; Models, Statistical; Neural Networks (Computer); Signal Processing, Computer-Assisted; Time Factors;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2008.918564
