Fuzzy Neural Network Integrated with PCA and Its Application in Raw Meal Grinding Process

被引：1
|
作者：
Qiao, Jinghui [1 ]
Chai, Tianyou [1 ]
Fang, Zheng [1 ]
Zhou, Xiaojie [1 ]
机构：
[1] Northeastern Univ, Ctr Automat Res, Shenyang 110004, Peoples R China
来源：
2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5 | 2010年
关键词：
Particle Size of Raw Meal; Raw Meal Grinding Process; Principal Component Analysis(PCA); Fuzzy Neural Network(FNN); MODEL;
D O I：
10.1109/CCDC.2010.5499084
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A fuzzy neural network model has been proposed and successfully applied to an annual clinker production capacity of 0.73 million ton of Jiuganghongda Cement Plant in China. Because the measurement values from raw meal grinding process are not independent, data sets with higher dimension increased model structure. Thus, a novel method based on fuzzy neural network(FNN) and principal component analysis (PCA) is discussed in detail. In this method, the PCA was applied to the model, which not only solved the linear correlation of the input variables, but also simplified the fuzzy neural network(FNN) structure and improved the training speed. Industrial application results show that the fuzzy neural network model has high accuracy and guidance to calciner temperature setting.
引用
收藏
页码：225 / 229
页数：5
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