A DELAY-DEPENDENT APPROACH TO ROBUST STABILITY FOR UNCERTAIN STOCHASTIC NEURAL NETWORKS WITH TIME-VARYING DELAY
This paper investigates the global delay-dependent robust stability in the mean square for uncertain stochastic neural networks with time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on a linear matrix inequality approach, globally delay-dependent robust stability criterion is derived by introducing some relaxation matrices which, when chosen properly, lead to a less conservative result. Two numerical examples are given to illustrate the effectiveness of the method.
Lu, Chien-Yu; Liao, Chin-Wen; Chang, Koan-Yuh; and Chang, Wen-Jer
"A DELAY-DEPENDENT APPROACH TO ROBUST STABILITY FOR UNCERTAIN STOCHASTIC NEURAL NETWORKS WITH TIME-VARYING DELAY,"
Journal of Marine Science and Technology: Vol. 18:
1, Article 9.
Available at: https://jmstt.ntou.edu.tw/journal/vol18/iss1/9