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 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.