DE_BPANN-Based Method for Predicting Smoke Components of Cigarette Made of Single Grade Tobacco

In order to analyze the relation between the chemical components in tobacco and the smoke components of cigarette,an artificial neural network based on differential evolution was used to predict the routine smoke components of cigarette made of single grade tobacco.An artificial neural network with single hidden layer was established.A differential evolution algorithm was used in the training process to avoid the shortcomings of artificial neural network based on error back propagation.This prediction method combined the local searching ability of artificial neural network and the global searching ability of differential evolution.On the basis of historical data of single grade tobacco and smoke of cigarettes made of single grade tobacco saved by a tobacco company,an artificial neural network prediction model was established,seven routine chemical components in the single grade tobacco were taken as input variables and the deliveries of tar,nicotine,carbon monoxide in mainstream cigarette smoke as output variables.The results showed that the root mean square errors of prediction of tar,nicotine,carbon monoxide reached a higher level and increased by 27%,10% and 26%,respectively,comparing with the traditional neural network.