Finite-time Mittag-Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions

doi:10.1088/1674-1056/ab7803

Abstract The finite-time Mittag-Leffler synchronization is investigated for fractional-order delayed memristive neural networks (FDMNN) with parameters uncertainty and discontinuous activation functions. The relevant results are obtained under the framework of Filippov for such systems. Firstly, the novel feedback controller, which includes the discontinuous functions and time delays, is proposed to investigate such systems. Secondly, the conditions on finite-time Mittag-Leffler synchronization of FDMNN are established according to the properties of fractional-order calculus and inequality analysis technique. At the same time, the upper bound of the settling time for Mittag-Leffler synchronization is accurately estimated. In addition, by selecting the appropriate parameters of the designed controller and utilizing the comparison theorem for fractional-order systems, the global asymptotic synchronization is achieved as a corollary. Finally, a numerical example is given to indicate the correctness of the obtained conclusions.

Keywords: fractional-order delayed memristive neural networks (FDMNN) parameters uncertainty discontinuous activation functions finite-time Mittag-Leffler synchronization

Received: 31 December 2019
Revised: 14 February 2020
Accepted manuscript online:

PACS:

02.30.Yy

(Control theory)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61703312 and 61703313).

Corresponding Authors: Zhixia Ding
E-mail: zxding89@163.com

Cite this article:

Chong Chen(陈冲), Zhixia Ding(丁芝侠), Sai Li(李赛), Liheng Wang(王利恒) Finite-time Mittag-Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions 2020 Chin. Phys. B 29 040202

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Finite-time Mittag-Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions

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