Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

• Published in: PloS one Vol. 12; no. 9; p. e0185007
• Main Authors: Chen, Chuan, Li, Lixiang, Peng, Haipeng, Yang, Yixian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.09.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial neural networks; Biology and Life Sciences; Chaos synchronization; Communications networks; Computer and Information Sciences; Computer Simulation; Controllers; Criteria; Delay; Engineering and Technology; Feedback; Feedback control; Fuzzy sets; Humans; Laboratories; Medicine and Health Sciences; Memory; Memristors; Methods; Neural networks; Neural Networks, Computer; Nonlinear Dynamics; Numerical simulations; Physical Sciences; Studies; Synchronism; Synchronization; Time Factors; Time synchronization; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0185007

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.