Graph-Theoretic Approach to Finite-Time Synchronization for Fuzzy Cohen–Grossberg Neural Networks with Mixed Delays and Discontinuous Activations

• Published in: Neural processing letters Vol. 52; no. 1; pp. 905 - 933
• Main Authors: Xu, Dongsheng, Xu, Chengqiang, Liu, Ming
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Complex Systems; Computational Intelligence; Computer Science; Control systems; Euclidean space; Feedback control; Fuzzy logic; Graph theory; Money markets; Neural networks; State feedback; Time synchronization; Finite-time synchronization; Switching state-feedback control; Distributed delays; Fuzzy Cohen–Grossberg neural networks
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-020-10237-4

This paper investigates finite-time synchronization for fuzzy Cohen–Grossberg neural networks (FCGNNs) with mixed delays and discontinuous activations via state-feedback control. The features of FCGNNs, discrete time delays, distributed delays and discontinuous activations are taken into account which makes our networks more general and practical in comparison with the most existing Cohen–Grossberg neural networks. Two switching state-feedback controllers designed for the implement of finite-time synchronization can be used to effectively overcome the limitations of the traditional continuous linear feedback controllers. Different from previous work, graph theory and Lyapunov method are used to study finite-time synchronization of FCGNNs for the first time in this paper, then some sufficient criteria are obtained to guarantee the finite-time synchronization of FCGNNs. In particular, it is worth noting that the settling time for finite-time synchronization is closely related to the topological structure of FCNNs. Finally, two numerical examples are given to verify the feasibility and effectiveness of the theoretical results.