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Study on Feature Selection and Identification Method of Tool Wear States Based on Svm

International Journal on Smart Sensing and Intelligent Systems
Volume 6 (2013): Issue 2 (January 2013)
By:
Open Access
|Apr 2013

Authors

Weilin Li

hiweilin@gmail.com

School of Mechanical Engineering Southwest Jiaotong University, China

Pan Fu

pfu@home.swjtu.edu.cn

School of Mechanical Engineering Southwest Jiaotong University, China

Weiqing Cao

School of Mechanical Engineering Southwest Jiaotong University, China
DOI: https://doi.org/10.21307/ijssis-2017-549 | Journal eISSN: 1178-5608
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Language: English
Page range: 448 - 465
Submitted on: Jan 24, 2013
Accepted on: Mar 12, 2013
Published on: Apr 10, 2013
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Tool condition monitoring,
feature selection,
multi-class support vector machine recursive feature elimination (SVM-RFE),
least squares support vector machines (LS-SVM)
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2013 Weilin Li, Pan Fu, Weiqing Cao, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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