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

• Published in: 2007 Chinese Control Conference pp. 409 - 412
• Main Author: Wang Jiesheng
• Format: Conference Proceeding
• Language: English
• 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
• ISBN: 9787811240559; 7811240556
• ISSN: 1934-1768
• DOI: 10.1109/CHICC.2006.4347471

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.