Dynamical behaviors analysis of memristor-based fractional-order complex-valued neural networks with time delay

Yuting Zhang, Yongguang Yu and Xueli Cui

Applied Mathematics and Computation, 2018, vol. 339, issue C, 242-258

Abstract: The robust stability for memristor-based fractional-order Complex-Valued Neural Networks (FCVNNs) with time delay is investigated here. In complex plane, by using the Lyapunov method, and under the sense of Filippov solutions, the existence of unique equilibrium and globally asymptotic stability for such Neural Networks (NNs) have been obtained when the nonlinear complex-valued activation functions could be split into two(real and imaginary) parts. Moreover, locally asymptotic stability for such Neural NNs have been proposed when the nonlinear complex activation functions are bounded. Lastly, three numerical examples are given to confirm the efficiency of theorems.

Keywords: Fractional-order; Complex-valued; Memristor; Time delay (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300318305319
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:339:y:2018:i:c:p:242-258

DOI: 10.1016/j.amc.2018.06.042

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().