CFNN Without Normalization-Based Acetone Product Quality Prediction.

This paper presents a kind of model based on compensatory fuzzy neural network (CFNN) without normalization to predict product quality in the acetone refining process. Important technological influence factors are selected according to the analysis results of several variables selection methods. Using the selected factors as the input variables of the network, a product quality prediction model is constructed. Experiment results show that the trained model achieves good effects, and has more advantages in convergence speed and error precision compared with CFNN with normalization. [ABSTRACT FROM AUTHOR]