基于BP神经网络的牡丹花热风干燥含水率预测模型

Moisture Content Prediction Modeling of Hot-air Drying for Pressed Peony Based on BP Neural Network

针对热风干燥制作牡丹压花时含水率不便实时测定的问题,探讨了干燥过程中热风温度、风速、压花板孔密度和牡丹花初始质量对干燥速率的影响。利用BP神经网络建立了干燥时间、热风温度、风速、牡丹花初始质量、压花板孔密度与牡丹花干燥过程中含水率之间的关系模型,采用Matlab神经网络工具箱对模型参数进行训练和模拟。结果表明,利用神经网络建立的模型仿真结果与实测值接近,预测性较好。

Pressed peony was made by hot-air drying method.The influence of temperature of hot-air,speed of hot-air,drying board＇s hole density and the initial mass of peony on drying speed was discussed.Relationship model between drying time,temperature of hot-air,speed of hot-air,drying board＇s hole density,the initial mass and moisture content was built by using BP neural network.Parameters in the proposed model were trained and simulated in Matlab.The results indicated that the simulated values of the drying moisture content were close to the measured values.