Intelligent Data Pretreatment Based on Principal Component Analysis and Fuzzy C-means Clustering in Flotation Process

A data pretreatment algorithm based on principal component analysis and fuzzy c-means clustering for flotation process is proposed in this paper. Linear regression of clustering centers gained by fuzzy c-means clustering algorithm is introduced to carry through data pretreatment. The process prior k...

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Bibliographic Details
Published in2007 Chinese Control Conference pp. 409 - 412
Main Author Wang Jiesheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2007
Subjects
Accuracy
Clustering algorithms
Data engineering
Data Pretreatment
Flotation Process
Fuzzy C-means Clustering (FCM)
Fuzzy control
Linear regression
Minerals
Partitioning algorithms
Predictive models
Principal component analysis
Principal Component Analysis (PCA)
Radial Basis Function (RBF
Radial basis function networks
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Summary:A data pretreatment algorithm based on principal component analysis and fuzzy c-means clustering for flotation process is proposed in this paper. Linear regression of clustering centers gained by fuzzy c-means clustering algorithm is introduced to carry through data pretreatment. The process prior knowledge and principal component analysis method are used to reduce dimensions of input vectors and to choose the secondary variables. Then the paper uses radial basis function neural network (RBFNN) to set up an inferential estimation model of quality indexes of flotation process aiming at principal component variables. The simulation results show that this inference estimation strategy has high predictive accuracy in flotation process.
ISBN:9787811240559
7811240556
ISSN:1934-1768
DOI:10.1109/CHICC.2006.4347471