Stability and synchronization of memristor-based fractional-order delayed neural networks

Liping Chen; Ranchao Wu; Jinde Cao; Jia-Bao Liu

doi:10.1016/j.neunet.2015.07.012

Stability and synchronization of memristor-based fractional-order delayed neural networks

Neural Netw. 2015 Nov:71:37-44. doi: 10.1016/j.neunet.2015.07.012. Epub 2015 Jul 31.

Authors

Liping Chen¹, Ranchao Wu², Jinde Cao³, Jia-Bao Liu²

Affiliations

¹ School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
² School of Mathematics, Anhui University, Hefei 230039, China.
³ Department of Mathematics, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: jdcao@seu.edu.cn.

PMID: 26282374
DOI: 10.1016/j.neunet.2015.07.012

Abstract

Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results.

Keywords: Fractional-order; Memristor-based neural networks; Stability; Synchronization.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Equipment Design
Feedback
Linear Models
Neural Networks, Computer*
Nonlinear Dynamics