Predictive model of production index for sugar clarification process by GDFNN

Abstract

Clarification process is significant to the cane sugar product, because its production index have direct effect on the output and quality of refined sugar. To maintain the index always in the range of expected value through adjusting operation parameters, an index predictive model is need. In this paper, the principle component analysis(PCA) and other statistical method were employed to deal with massive field data first, then built the generalized dynamic fuzzy neural network(GDFNN) predictive model, and finally the new model was compared with the back propagation(BP) network one on various performances. © 2012 Springer-Verlag.