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PLoS One. 2017 Sep 20;12(9):e0185007. doi: 10.1371/journal.pone.0185007. eCollection 2017.

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

Author information

1
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
2
State Key Laboratory of Public Big Data, Guizhou 550025, China.

Abstract

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.

PMID:
28931066
PMCID:
PMC5607209
DOI:
10.1371/journal.pone.0185007
[Indexed for MEDLINE]
Free PMC Article
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