Back to Search Start Over

Sinusoidal disturbance induced topology identification of Hindmarsh-Rose neural networks

Authors :
Junchan Zhao
Nathalie Corson
Cyrille Bertelle
M. A. Aziz-Alaoui
The School of Mathematics and Statistics, Hunan University of Commerce
Laboratoire de Mathématiques Appliquées du Havre (LMAH)
Université Le Havre Normandie (ULH)
Normandie Université (NU)-Normandie Université (NU)
Equipe Réseaux d'interactions et Intelligence Collective (RI2C - LITIS)
Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS)
Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie)
Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN)
Normandie Université (NU)-Université Le Havre Normandie (ULH)
Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie)
Normandie Université (NU)
Source :
Science in China Series F: Information Sciences, Science in China Series F: Information Sciences, Springer Verlag, 2016, 59 (11), ⟨10.1007/s11432-015-0915-9⟩
Publication Year :
2016
Publisher :
Springer Science and Business Media LLC, 2016.

Abstract

International audience; Topology identification of complex networks is an important problem. Existing research shows that the synchronization of network nodes is an obstacle in the identification of network topology. Identification of the structure of the network presents an interesting challenge during the synchronization of complex networks. We developed a new method using the sinusoidal disturbance to identify the topology when the complex network achieves synchronization. Compared with the disturbance of all the nodes, the disturbance of the key nodes alone can achieve a very good effect. Finally, numerical simulation data are provided to validate our hypothesis.

Details

ISSN :
18691919, 1674733X, 10092757, and 18622836
Volume :
59
Database :
OpenAIRE
Journal :
Science China Information Sciences
Accession number :
edsair.doi.dedup.....e2879a67fc956506afb7de15365cec52
Full Text :
https://doi.org/10.1007/s11432-015-0915-9