Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays.

Published

Journal Article

This paper deals with the problem of global exponential synchronization of a class of memristor-based recurrent neural networks with time-varying delays based on the fuzzy theory and Lyapunov method. First, a memristor-based recurrent neural network is designed. Then, considering the state-dependent properties of the memristor, a new fuzzy model employing parallel distributed compensation (PDC) gives a new way to analyze the complicated memristor-based neural networks with only two subsystems. Comparisons between results in this paper and in the previous ones have been made. They show that the results in this paper improve and generalized the results derived in the previous literature. An example is also given to illustrate the effectiveness of the results.

Full Text

Duke Authors

Cited Authors

  • Wen, S; Bao, G; Zeng, Z; Chen, Y; Huang, T

Published Date

  • December 2013

Published In

Volume / Issue

  • 48 /

Start / End Page

  • 195 - 203

PubMed ID

  • 24216502

Pubmed Central ID

  • 24216502

Electronic International Standard Serial Number (EISSN)

  • 1879-2782

International Standard Serial Number (ISSN)

  • 0893-6080

Digital Object Identifier (DOI)

  • 10.1016/j.neunet.2013.10.001

Language

  • eng