TY - JOUR
T1 - LMI-based asymptotic stability analysis of neural networks with time-varying delays
AU - Li, Tao
AU - Sun, Changyin
AU - Zhao, Xianlin
AU - Lin, Chong
PY - 2008/6
Y1 - 2008/6
N2 - The problem of the global asymptotic stability for a class of neural networks with time-varying delays is investigated in this paper, where the activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By constructing suitable Lyapunov functionals and combining with linear matrix inequality (LMI) technique, new global asymptotic stability criteria about different types of time-varying delays are obtained. It is shown that the criteria can provide less conservative result than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
AB - The problem of the global asymptotic stability for a class of neural networks with time-varying delays is investigated in this paper, where the activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By constructing suitable Lyapunov functionals and combining with linear matrix inequality (LMI) technique, new global asymptotic stability criteria about different types of time-varying delays are obtained. It is shown that the criteria can provide less conservative result than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
KW - Delay-dependent
KW - Global asymptotic stability
KW - Linear matrix inequality (LMI)
KW - Neural networks (NNs)
UR - https://www.scopus.com/pages/publications/46649101600
U2 - 10.1142/S0129065708001567
DO - 10.1142/S0129065708001567
M3 - 文章
C2 - 18595153
AN - SCOPUS:46649101600
SN - 0129-0657
VL - 18
SP - 257
EP - 265
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
IS - 3
ER -