Periodicity and dissipativity for memristor-based mixed time-varying delayed neural networks via differential inclusions.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 24892272.
- Also identified by DOI 10.1016/j.neunet.2014.05.002.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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.
Medical subject headings
- Neural Networks, Computer