Resilient fault-tolerant anti-synchronization for stochastic delayed reaction–diffusion neural networks with semi-Markov jump parameters

• Published in: Neural networks Vol. 125; pp. 194 - 204
• Main Authors: Zhou, Jianping, Liu, Yamin, Xia, Jianwei, Wang, Zhen, Arik, Sabri
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2020
• Subjects: Anti-synchronization; Fault-tolerant control; Neural networks; Resilient control; Semi-Markov process; Resilient control; Semi-Markov process; Anti-synchronization; Fault-tolerant control; Neural networks
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2020.02.015

This paper deals with the anti-synchronization issue for stochastic delayed reaction–diffusion neural networks subject to semi-Markov jump parameters. A resilient fault-tolerant controller is utilized to ensure the anti-synchronization in the presence of actuator failures as well as gain perturbations, simultaneously. Firstly, by means of the Lyapunov functional and stochastic analysis methods, a mean-square exponential stability criterion is derived for the resulting error system. It is shown the obtained criterion improves a previously reported result. Then, based on the present analysis result and using several decoupling techniques, a strategy for designing the desired resilient fault-tolerant controller is proposed. At last, two numerical examples are given to illustrate the superiority of the present stability analysis method and the applicability of the proposed resilient fault-tolerant anti-synchronization control strategy, respectively.