Deep learning controller design of embedded control system for maglev train via deep belief network algorithm-Reference-Cited by-同舟云学术

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Deep learning controller design of embedded control system for maglev train via deep belief network algorithm

Published:2020-04-09 Issue:3 Volume:24 Page:161-181
ISSN:0929-5585
Container-title:Design Automation for Embedded Systems
language:en
Short-container-title:Des Autom Embed Syst

Author:

Gao Ding-gang,Sun You-gang,Luo Shi-hui,Lin Guo-bin,Tong Lai-sheng

Funder

the National Natural Science Foundation of China

China Postdoctoral Science Foundation

Independent Research Project of State Key Laboratory

Funded Project of National Key Research and Development Plan

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Software

Link

http://link.springer.com/content/pdf/10.1007/s10617-020-09237-3.pdf

Reference27 articles.

1. Thornton RD (2009) Efficient and affordable maglev opportunities in the United States. Proc IEEE 97:1901–1921

2. Sun Y, Xu J, Qiang H, Chen C, Lin G (2019) Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method. Measurement 141:217–226

3. Boldea I, Tutelea L, Xu W et al (2017) Linear electric machines, drives, and MAGLEVs: an overview. IEEE Trans Ind Electron 65(9):7504–7515

4. Sun Y, Xu J, Qiang H, Lin G (2019) Adaptive neural-fuzzy robust position control scheme for maglev train systems with experimental verification. IEEE Trans Ind Electron 66(11):8589–8599

5. Sun Y, Xu J, Qiang H, Wang W, Lin G (2019) Hopf bifurcation analysis of maglev vehicle–guideway interaction vibration system and stability control based on fuzzy adaptive theory. Comput Ind 108:197–209

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