Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach

Hui Zhao, Lixiang Li, Haipeng Peng, Jurgen Kurths, Jinghua Xiao, Yixian Yang

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

In this paper, exponential anti-synchronization in mean square of an uncertain memristor-based neural network is studied. The uncertain terms include non-modeled dynamics with boundary and stochastic perturbations. Based on the differential inclusions theory, linear matrix inequalities, Gronwall’s inequality and adaptive control technique, an adaptive controller with update laws is developed to realize the exponential anti-synchronization. Adaptive controller can adjust itself behavior to get the best performance, according to the environment is changing or the environment has changed, which has the ability to adapt to environmental change. Furthermore, a numerical example is provided to validate the effectiveness of the proposed method
Original languageEnglish
Article number109
Number of pages10
JournalThe European Physical Journal B - Condensed Matter and Complex Systems
Volume88
Early online date4 May 2015
DOIs
Publication statusPublished - May 2015

Fingerprint

Memristors
adaptive control
synchronism
controllers
Synchronization
Neural networks
Controllers
Linear matrix inequalities
inclusions
perturbation

Keywords

  • Statistical and Nonlinear Physics

Cite this

Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach. / Zhao, Hui; Li, Lixiang; Peng, Haipeng; Kurths, Jurgen; Xiao, Jinghua; Yang, Yixian.

In: The European Physical Journal B - Condensed Matter and Complex Systems , Vol. 88, 109, 05.2015.

Research output: Contribution to journalArticle

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