Fuzzy neural network integrated with PCA and its application in raw meal grinding process

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.