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 journalArticlepeer-review

30 Citations (Scopus)


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
Early online date4 May 2015
Publication statusPublished - May 2015

Bibliographical note

The authors thank all the editor and the anonymous referees for their constructive comments and valuable suggestions, which are helpful to improve the quality of this paper. The work is supported by the National Natural Science Foundation of China (Grant Nos. 61170269, 61472045), the Beijing Higher Education Young Elite Teacher Project (Grant No. YETP0449), and the Beijing Natural Science Foundation (Grant No. 4142016).
All authors contributed equally to the paper.


  • Statistical and Nonlinear Physics


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