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Proceedings Article

The processing quality prediction based on the OHIF Elman neural network

[+] Author Affiliations
Jie Yang

South China Univ. of Technology (China) and Guangdong Univ. of Technology (China)

Guixiong Liu

South China Univ. of Technology (China)

Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973P (May 26, 2011); doi:10.1117/12.889145
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From Conference Volume 7997

  • Fourth International Seminar on Modern Cutting and Measurement Engineering
  • Jiezhi Xin; Lianqing Zhu; Zhongyu Wang
  • Beijing, China | December 10, 2010

abstract

Quality prediction and control methods are crucial in acquiring safe and reliable operation in process quality control. Considering The standard Elman neural network model only effective for the low-level static system, then a new OHIF Elman is proposed in this paper, three different feedback factor are introduced into the hidden layer, associated layer, and output layer of the Elman neural network. In order to coordinate the efficiency of prediction accuracy and prediction, LM-CGD mixed algorithm is used for training the network model. The simulation and experiment results show the quality model can effectively predict the characteristic values of process quality, and it also can identify abnormal change pattern and enhance process control accuracy.

© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Jie Yang and Guixiong Liu
"The processing quality prediction based on the OHIF Elman neural network", Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973P (May 26, 2011); doi:10.1117/12.889145; http://dx.doi.org/10.1117/12.889145


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