NOVEL STOCHASTIC STABILITY CONDITIONS OF FUZZY NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS UNDER IMPULSIVE PERTURBATIONS

作者：Zhang, Y.; Zheng, C. -D.^*

来源：Neural Network World, 2016, 26(1): 7-28.

DOI：10.14311/NNW.2016.26.001

• impulse
• fuzzy neural networks
• Markovian jumping
• linear convex combination technique
• reciprocal convex combination

This paper investigates the stochastic stability of fuzzy neural networks with Markovian jumping parameters and mixed delays under impulsive perturbations in mean square. The mixed delays consist of time-varying delay and continuously distributed delay. By employing a new Lyapunov-Krasovskii functional, linear convex combination technique, a novel reciprocal convex lemma and the free-weight matrix method, two novel sufficient conditions are derived to ensure the stochastic asymptotic stability of the equilibrium point of the considered networks in mean square. The proposed results, which are expressed in terms of linear matrix inequalities, can be easily checked via Matlab LMI Toolbox. Finally, two numerical examples are given to demonstrate the effectiveness and less conservativeness of our theoretical results over existing literature.

• 出版日期2016
• 单位大连交通大学