Finite-Time Synchronization of Fractional-Order Fuzzy Time-Varying Coupled Neural Networks Subject to Reaction-Diffusion

• Published in: IEEE transactions on fuzzy systems Vol. 31; no. 10; pp. 1 - 10
• Main Authors: Xu, Yao, Liu, Wenxi, Wu, Yongbao, Li, Wenxue
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Artificial neural networks; Couplings; Estimation; Feedback control; Finite-time synchronization; Fractional-order networks; Fuzzy control; Fuzzy logic; Fuzzy time-varying coupled neural networks; Graph theory; Liapunov functions; Lyapunov methods; Mathematical models; Neural networks; Neurons; Reaction-diffusion; Synchronization; Time synchronization
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2023.3257100

In this article, finite-time synchronization is investigated for fractional-order fuzzy time-varying coupled neural networks subject to reaction-diffusion by establishing a new framework under fuzzy-based feedback control and fuzzy-based adaptive control. For the considered networks, we put forward an innovative graph-theory-based time-varying Lyapunov function. To overcome the difficulty of estimating the fractional derivative of this function, this paper proposes a novel fractional derivative rule. Through graph theory and the Lyapunov method, several finite-time synchronous criteria are obtained for the considered networks, and the estimation of the settling time is derived. Finally, the numerical results are shown to demonstrate the practicability of the given results.