A Model of BP Neural Network Optimized on Particle Swarm Algorithm for Predicting Ratio of Potassium to Chlorine in Flue-cured Tobacco

An optimized model of BP neural network for predicting the ratio of potassium to chlorine(K/Cl ratio) in flue-cured tobacco was established by using the measurable factors influencing K/Cl ratio as network inputs,selecting appropriate network parameters with empirical method,and optimizing initial weights and thresholds of neural network with particle swarm algorithm.The results showed that the total correlation coefficient between the prediction value of the test sample and the expected value was 0.97155,which increased by 13.77%,and the samples with the error within -1,1 accounted for 86.67%.This model features good fitting ability,certain predictive ability,and significantly improved generalization ability,it is helpful to the evaluation of smoking quality and combustibility of tobacco leaves.