Article
Finite-time Anti-synchronization of Memristive Stochastic BAM Neural Networks with Probabilistic Time-varying Delays
Chaos Solitons and Fractals
ORCID ID
https://orcid.org/0000-0002-3202-1127; Wenbing Zhao
Document Type
Article
Publication Date
8-1-2018
Disciplines
Abstract
This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism.
DOI
10.1016/j.chaos.2018.06.013
Version
Postprint
Creative Commons License
Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International
Citation Information
Manman Yuan, Weiping Wang, Xiong Luo, Linlin Liu, et al.. "Finite-time Anti-synchronization of Memristive Stochastic BAM Neural Networks with Probabilistic Time-varying Delays" Chaos Solitons and Fractals Vol. 133 (2018) p. 244 - 260 Available at: http://works.bepress.com/wenbingzhao/52/
Link to publisher version:
https://doi.org/10.1016/j.chaos.2018.06.013
This work was supported by the National Key Research and Development Program of China under Grants 2017YFB0702300, the State Scholarship Fund of China Scholarship Council (CSC), the National Natural Science Foundation of China under Grants 61603032 and 61174103, the Fundamental Research Funds for the Central Universities under Grant 06500025, the National Key Technologies R&D Program of China under Grant 2015BAK38B01, and the University of Science and Technology Beijing-National Taipei University of Technology Joint Research Program under Grant TW201705.