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Chin. Opt. Lett.
 Home  List of Issues    Issue 09 , Vol. 15 , 2017    10.3788/COL201715.091201


LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor
Li Fu1, Jun Luo1, Weimin Chen1, Xueming Liu2, Dong Zhou1, Zhongling Zhang1, and Sheng Li1
1 Key Lab of Optoelectronic Technology &
Systems of Ministry of Education,[ Chongqing University], Chongqing 400044, China
2 [5011 District Measurement Station of Weapon Industry], Chongqing 400050, China

Chin. Opt. Lett., 2017, 15(09): pp.091201

DOI:10.3788/COL201715.091201
Topic:Instrumentation, measurement and metrology
Keywords(OCIS Code): 120.6660  060.2370  290.5820  

Abstract
Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces, but their measuring precision and efficiency need to be improved further. A least-squares support vector machine (LS-SVM)-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing (CSA) and standard simplex (SS) methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is ?4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.

Copyright: © 2003-2012 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Received:2017/2/27
Accepted:2017/5/25
Posted online:2017/6/23

Get Citation: Li Fu, Jun Luo, Weimin Chen, Xueming Liu, Dong Zhou, Zhongling Zhang, and Sheng Li, "LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor," Chin. Opt. Lett. 15(09), 091201(2017)

Note: The authors gratefully acknowledge Prof. Dr. Chih-Jen Lin for the LIBSVM toolbox and Prof. Dr. J. Suykens for the LS-SVMlab1.8 toolkit.



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