The synchronization and stability analysis of delayed fuzzy Cohen-Grossberg neural networks via nonlinear measure method

ABSTRACT

This paper examines the problem of master-slave synchronization for a class of fuzzy Cohen-Grossberg neural networks (FCGNNs) subject to fuzzy effects and time-delays (time-varying and distributed). Some sufficient and new conditions are given in order to establish the exponential lag synchronization for the considered model. Also, the existence, the uniqueness, and exponential stability of the equilibrium point are investigated, based on the nonlinear measure method and Halanay inequality. Finally, two examples with numerical simulations are given to show the effectiveness of the derived results.

KEYWORDS:

• Global exponential stability
• lag synchronization
• fuzzy Cohen-Grossberg neural networks
• nonlinear measure method

Acknowledgments

The authors would like to thank the anonymous reviewers and the editor for their constructive comments, which greatly improved the quality of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.