Journal
NEURAL NETWORKS
Volume 57, Issue -, Pages 12-22Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2014.05.002
Keywords
Memristor-based neural networks; Mawhin-like coincidence theorem; Periodicity; Dissipativity
Funding
- National Natural Science Foundation of China [11371127]
- Hunan Provincial Innovation Foundation
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In this paper, we investigate a class of memristor-based neural networks with general mixed delays involving both time-varying delays and distributed delays. By using the Mawhin-like coincidence theorem, together with the differential inclusion theory, M-matrix properties and differential inequality techniques, some novel criteria are established for ensuring the periodicity and dissipativity for the addressed neural networks. Finally, two numerical examples with simulations are presented to demonstrate the effectiveness of the theoretical results. (C) 2014 Elsevier Ltd. All rights reserved.
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