Control chart pattern recognition using the convolutional neural network-Reference-Cited by-同舟云学术

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Control chart pattern recognition using the convolutional neural network

Published:2019-04-11 Issue:3 Volume:31 Page:703-716
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Zan Tao,Liu Zhihao^ORCID,Wang Hui,Wang Min,Gao Xiangsheng

Funder

National Natural Science Foundation of China

Beijing Municipal Commission of Education

National Fund for Studying Abroad

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/content/pdf/10.1007/s10845-019-01473-0.pdf

Reference44 articles.

1. Addeh, A., Khormali, A., & Golilarz, N. A. (2018). Control chart pattern recognition using RBF neural network with new training algorithm and practical features. ISA Transactions. https://doi.org/10.1016/j.isatra.2018.04.020 .

2. Ajm, T., & Hulzebosch, A. A. (1996). Computer vision system for on-line sorting of pot plants using an artificial neural network classifier. Computers and Electronics in Agriculture,15(1), 41–55.

3. Al-Assaf, Y. (2004). Recognition of control chart patterns using multiresolution wavelets analysis and neural networks. Computers & Industrial Engineering,47(1), 17–29.

4. Assaleh, K., & Al-Assaf, Y. (2005). Features extraction and analysis for classifying causable patterns in control charts. Computers & Industrial Engineering,49(1), 168–181.

5. Awadalla, M. H. A., & Sadek, M. A. (2012). Spiking neural network-based control chart pattern recognition. Alexandria Engineering Journal,51(1), 27–35.

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