ORCID ID

https://orcid.org/0000-0002-3202-1127; Wenbing Zhao

Document Type

Article

Publication Date

8-2018

Publication Title

Chaos Solitons and Fractals

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.

Comments

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.

DOI

10.1016/j.chaos.2018.06.013

Version

Postprint

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Volume

133

Available for download on Saturday, August 01, 2020

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