4.6 Article

Passivity of fractional-order coupled neural networks with multiple state/derivative couplings

Journal

NEUROCOMPUTING
Volume 455, Issue -, Pages 379-389

Publisher

ELSEVIER
DOI: 10.1016/j.neucom.2021.05.050

Keywords

Fractional-order coupled neural networks; (FOCNNs); Multiple derivative couplings; Multiple state couplings; Passivity; Synchronization

Funding

  1. National Natural Science Foundation of China [61773285]
  2. Tianjin Talent Development Special Support Program for Young Top-Notch Talent
  3. Natural Science Foundation of Tianjin, China [19JCYBJC18700]
  4. Program for Innovative Research Team in University of Tianjin [TD13-5032]

Ask authors/readers for more resources

This paper discusses the passivity and synchronization criteria for fractional-order coupled neural networks with multiple state couplings and multiple derivative couplings. By using the Lyapunov method and output-strict passivity, the synchronization issues for these networks are addressed effectively.
This paper respectively discusses the passivity of fractional-order coupled neural networks with multiple state couplings (FOCNNMSCs) or multiple derivative couplings (FOCNNMDCs). In light of fractional-order system theory, several sufficient conditions for ensuring the passivity of the FOCNNMSCs are established. Moreover, a synchronization criterion for the FOCNNMSCs is obtained under the condition that the FOCNNMSCs is output-strictly passive. Similarly, we also investigate the passivity of the FOCNNMDCs by resorting to the Lyapunov functional method, and the output-strict passivity is used to deal with the synchronization for the FOCNNMDCs. Finally, the effectiveness of the obtained criteria is verified by two numerical examples with simulations. (c) 2021 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available