A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction-Reference-Cited by-同舟云学术

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A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction

Published:2018-01-05 Issue:5 Volume:30 Page:2245-2256
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Bai Yun,Sun Zhenzhong,Zeng Bo,Long Jianyu,Li Lin,de Oliveira José Valente,Li Chuan

Funder

Postdoctoral Science Foundation of China

Research Program of Higher Education of Guangdong

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/content/pdf/10.1007/s10845-017-1388-1.pdf

Reference39 articles.

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2. Akashi, K., & Kunitomo, N. (2015). The limited information maximum likelihood approach to dynamic panel structural equation models. Annals of the Institute of Statistical Mathematics, 67(1), 39–73.

3. Bai, Y., & Li, C. (2016). Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach. Energy and Buildings, 127, 571–579.

4. Bai, Y., Sun, Z. Z., Zeng, B., Deng, J., & Li, C. (2017). A multi-pattern deep fusion model for short-term bus passenger flow forecasting. Applied Soft Computing, 58, 669–680.

5. Bustillo, A., & Correa, M. (2012). Using artificial intelligence to predict surface roughness in deep drilling of steel components. Journal of Intelligence Manufacturing, 23(5), 1893–1902.

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