Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays
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 distribu...
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Published in | PloS one Vol. 12; no. 9; p. e0185007 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
20.09.2017
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors have declared that no competing interests exist. |
ISSN: | 1932-6203 1932-6203 |
DOI: | 10.1371/journal.pone.0185007 |