BP神经网络在玉米干燥含水率预测中的研究

Study on Prediction of Corn Drying Moisture Content Based on BP Neural Network

针对粮食干燥后含水率不一致这一问题,本研究根据农场测得实验数据为依据,采用入机初始含水率,烘干塔热风温度,入机玉米温度为输入量,排粮电机转速为输出量,构建BP神经网络玉米出机含水率预测模型。实验证明,通过与实际排粮电机转速作比较,该模型预测排粮电机转速误差在-5~5 r/min,预测值和实际值相关系数R为0.98419,证明该模型可以有效预测排粮电机转速和出机玉米含水率。

In view of the inconsistency of moisture content after grain drying, taking the continuous cross flow drying tower as the experimental object and based on the experimental data measured on the farm, the BP neural network prediction model of corn moisture content out of the machine is constructed. The initial moisture content of corn into the machine, the hot air temperature of drying tower, the temperature of corn into the machine are used as the input and the rotation speed of grain discharge motor is used as the output. Compared with the actual speed, the model can effectively predict the speed of grain discharge motor and the moisture content of corn.