TY - JOUR
T1 - Global exponential stability for discrete-time neural networks with variable delays
AU - Chen, Wu-Hua
AU - Lu, Xiaomei
AU - Liang, Dong-Ying
PY - 2006
Y1 - 2006
N2 - This Letter provides new exponential stability criteria for discrete-time neural networks with variable delays. The main technique is to reduce exponential convergence estimation of the neural network solution to that of one component of the corresponding solution by constructing Lyapunov function based on M-matrix. By introducing the tuning parameter diagonal matrix, the delay-independent and delay-dependent exponential stability conditions have been unified in the same mathematical formula. The effectiveness of the new results are illustrated by three examples.
AB - This Letter provides new exponential stability criteria for discrete-time neural networks with variable delays. The main technique is to reduce exponential convergence estimation of the neural network solution to that of one component of the corresponding solution by constructing Lyapunov function based on M-matrix. By introducing the tuning parameter diagonal matrix, the delay-independent and delay-dependent exponential stability conditions have been unified in the same mathematical formula. The effectiveness of the new results are illustrated by three examples.
UR - http://handle.uws.edu.au:8081/1959.7/538402
U2 - 10.1016/j.physleta.2006.05.014
DO - 10.1016/j.physleta.2006.05.014
M3 - Article
SN - 0375-9601
VL - 358
SP - 186
EP - 198
JO - Physics Letters, Section A: General, Atomic and Solid State Physics
JF - Physics Letters, Section A: General, Atomic and Solid State Physics
IS - 3
ER -