Multiple T-S fuzzy neural networks soft sensing modeling of flotation process based on fuzzy c-means clustering algorithm

Abstract

Inspired by the idea of combining multiple models to improve prediction accuracy and robustness, a soft sensing modeling of flotation process based on multiple T-S fuzzy neural networks and fuzzy c-means clustering algorithm (FCM) is proposed. Firstly, the model adopts principal component analysis (PCA) to reduce dimensions of the input variables data composed of texture characteristics of floatation froth image and process variables. FCM algorithm is used for separating a whole training data set into several clusters with different centers and each subset is trained by T-S FNN. The degrees of membership are used for combining several models to obtain the finial soft sensing result. Simulation results show that the proposed modeling is effective in the prediction of indexes and meets the requirement for optimization computation for the flotation process. © 2010 Springer-Verlag Berlin Heidelberg.