Input-to-state stability of stochastic nonlinear fuzzy Cohen–Grossberg neural networks with the event-triggered control

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.

KEYWORDS:

• Cohen–Grossberg neural network
• stochastic nonlinear fuzzy system
• input-to-state stability
• event-triggered control
• internal execution time interval

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was jointly supported by the National Natural Science Foundation of China [61773217, 61374080, 11701288], the Natural Science Foundation of Jiangsu Province [BK20161552], Qing Lan Project of Jiangsu Province, the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX17_1074] and the Outstanding Doctoral Dissertation Topic-Selection Project of Nanjing Normal University [YXXT17_006].