Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

PLoS One. 2017 Sep 20;12(9):e0185007. doi: 10.1371/journal.pone.0185007. eCollection 2017.

Abstract

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

MeSH terms

  • Algorithms*
  • Computer Simulation*
  • Feedback*
  • Humans
  • Neural Networks, Computer*
  • Nonlinear Dynamics
  • Time Factors

Grants and funding

The work is supported by the National Key Research and Development Program (Grant nos. 2016YFB0800602 and 2016YFB0800604), and the National Natural Science Foundation of China (Grant Nos. 61573067, 61771071 and 61472045).