Paper Title:
Quality Prediction Based on the OHIF Elman Neural Network for Key Process
  Abstract

Quality prediction and control methods are crucial in acquiring safe and reliable operation in process quality control. A hierarchical multiple criteria decision model is established for the key process and the weight matrix method stratified is discussed, and then KPCA is used to eliminate minor factors and to extract major factors among so many quality variables. 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.

  Info
Periodical
Advanced Materials Research (Volumes 271-273)
Edited by
Junqiao Xiong
Pages
713-718
DOI
10.4028/www.scientific.net/AMR.271-273.713
Citation
J. Yang, G. X. Liu, "Quality Prediction Based on the OHIF Elman Neural Network for Key Process", Advanced Materials Research, Vols. 271-273, pp. 713-718, 2011
Online since
July 2011
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Price
$32.00
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