Attractivity analysis of memristor-Based cellular neural networks with time-Varying delays

Zhenyuan Guo, Jun Wang, Zheng Yan

Research output: Contribution to journalArticlepeer-review

164 Citations (Scopus)


This paper presents new theoretical results on the invariance and attractivity of memristor-based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions to assure the boundedness and global attractivity of the networks are derived. Using state-space decomposition and some analytic techniques, it is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2 n to 22n2+n22n2(times}) compared with that without a memristor. In addition, sufficient conditions for the invariance and local or global attractivity of equilibria or attractive sets in any designated region are derived. Finally, two illustrative examples are given to elaborate the characteristics of the results in detail.

Original languageEnglish
Article number6603322
Pages (from-to)704-717
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
Publication statusPublished - Apr 2014
Externally publishedYes


  • Attractivity
  • Cellular neural network
  • Equilibrium
  • Invariance
  • Memristor


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