A DELAY-DEPENDENT APPROACH TO ROBUST STABILITY FOR UNCERTAIN STOCHASTIC NEURAL NETWORKS WITH TIME-VARYING DELAY

Authors

Chien-Yu Lu, Department of Industrial Education and Technology, National Changhua University of Education, Changhua, Taiwan 500, R.O.C
Chin-Wen Liao, Department of Industrial Education and Technology, National Changhua University of Education, Changhua, Taiwan 500, R.O.C
Koan-Yuh Chang, Department of Electronic Engineering, Chienkuo Technology University, Changhua 500, Taiwan, R.O.C
Wen-Jer Chang, Department of Marine Engineering, National Taiwan Ocean University, Keelung 202, Taiwan, R.O.C.Follow

Abstract

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.

Recommended Citation

Lu, Chien-Yu; Liao, Chin-Wen; Chang, Koan-Yuh; and Chang, Wen-Jer (2010) "A DELAY-DEPENDENT APPROACH TO ROBUST STABILITY FOR UNCERTAIN STOCHASTIC NEURAL NETWORKS WITH TIME-VARYING DELAY," Journal of Marine Science and Technology: Vol. 18: Iss. 1, Article 9.
DOI: 10.51400/2709-6998.1867
Available at: https://jmstt.ntou.edu.tw/journal/vol18/iss1/9