Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 8; pp. 3938 - 3947
• Main Authors: Yu, Tianhu, Cao, Jinde, Rutkowski, Leszek, Luo, Yi-Ping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-08-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological neural networks; Complex-valued memristive neural networks (CVMNNs); differential inequality; finite-time stability (FTS); finite-time synchronization; Hybrid control; impulsive control; Memristors; Multi-layer neural network; Neural networks; Neurons; Perturbation methods; Stability criteria; Synchronization; Time synchronization
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2021.3054967

The finite-time synchronization problem is investigated for the master-slave complex-valued memristive neural networks in this article. A novel Lyapunov-function based finite-time stability criterion with impulsive effects is proposed and utilized to design the decentralized finite-time synchronization controller. Not only the settling time but also the attractive domain with respect to the impulsive gain and average impulsive interval, as well as initial values is derived according to the sufficient synchronization condition. Two examples are outlined to illustrate the validity of our hybrid control strategy.