Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

Mingwen Zheng; Lixiang Li; Haipeng Peng; Jinghua Xiao; Yixian Yang; Yanping Zhang; Hui Zhao

doi:10.1371/journal.pone.0191473

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

PLoS One. 2018 Jan 25;13(1):e0191473. doi: 10.1371/journal.pone.0191473. eCollection 2018.

Authors

Mingwen Zheng^{1

2}, Lixiang Li³, Haipeng Peng³, Jinghua Xiao^{1

4}, Yixian Yang³, Yanping Zhang², Hui Zhao⁵

Affiliations

¹ School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
² School of Mathematics and Statistics, Shandong University of Technology, Zibo 255000, China.
³ Information Security Center, State Key Laboratory of Networking and Switching Technology, National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China.
⁴ State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China.
⁵ Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China.

Abstract

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

Publication types

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

MeSH terms

Computer Simulation
Feedback
Models, Neurological
Neural Networks, Computer*
Nonlinear Dynamics
Time Factors

Grants and funding

This paper is supported by the National Key Research and Development Program (Grant Nos. 2016YFB0800602) to LL, the National Natural Science Foundation of China (Grant Nos. 61472045 to YY, 61573067 to LL, 61771071 to HP) (URL: http://www.nsfc.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.