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CLC number: TH7; TM15

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Received: 2008-07-25

Revision Accepted: 2009-03-05

Crosschecked: 2009-05-27

Cited: 5

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.8 P.1205-1212


Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm

Author(s):  Hong-li QI, Hui ZHAO, Wei-wen LIU, Hai-bo ZHANG

Affiliation(s):  Department of Instrument Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   huizhao@sjtu.edu.cn

Key Words:  Grating eddy current displacement sensor (GECDS), Artificial neural network (ANN), Genetic algorithm (GA), Parameters optimization, Nonlinearity error

Hong-li QI, Hui ZHAO, Wei-wen LIU, Hai-bo ZHANG. Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm[J]. Journal of Zhejiang University Science A, 2009, 10(8): 1205-1212.

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author="Hong-li QI, Hui ZHAO, Wei-wen LIU, Hai-bo ZHANG",
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publisher="Zhejiang University Press & Springer",

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%T Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm
%A Hong-li QI
%A Wei-wen LIU
%A Hai-bo ZHANG
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%N 8
%P 1205-1212
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820564

T1 - Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm
A1 - Hong-li QI
A1 - Hui ZHAO
A1 - Wei-wen LIU
A1 - Hai-bo ZHANG
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 8
SP - 1205
EP - 1212
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820564

A grating eddy current displacement sensor (GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions. The parameters optimization of the sensor is essential for economic and efficient production. This paper proposes a method to combine an artificial neural network (ANN) and a genetic algorithm (GA) for the sensor parameters optimization. A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS, and then a GA is used in the optimization process to determine the design parameter values, resulting in a desired minimal nonlinearity error of about 0.11%. The calculated nonlinearity error is 0.25%. These results show that the proposed method performs well for the parameters optimization of the GECDS.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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