Input-to-state stability of stochastic nonlinear fuzzy Cohen–Grossberg neural networks with the event-triggered control. Issue 9 (1st September 2020)

• Record Type: Journal Article
• Authors: Xie, Wenjuan; Zhu, Quanxin
• Is Part Of: International journal of control. Volume 93:Issue 9(2020)
• Page Start: 2043
• Page End: 2052
• Publication Date: 2020-09-01
• Subjects: Cohen–Grossberg neural network -- stochastic nonlinear fuzzy system -- input-to-state stability -- event-triggered control -- internal execution time interval; Automatic control -- Periodicals; Electronic journals; 629.8
• DOI: 10.1080/00207179.2018.1540887
• Languages: English
• ISSNs: 0020-7179

Abstract

ABSTRACT: In this paper, we investigate a class of stochastic nonlinear fuzzy Cohen–Grossberg neural networks with feedback control and an unknown exogenous disturbance. By using the Lyapunov function, Itô's formula, Dynkin's formula, Comparison principle and stochastic analysis theory, we show that the considered system is input-to-state stable with the help of the designed event-triggered mechanism. Moreover, we also guarantee that the internal execution time intervals of control task will not be arbitrarily small. Finally, some remarks and discussions have been provided to show that our results are meaningful.