基于BP神经网络的5HNH-15干燥机出粮水分研究  

Research on Grain Moisture of 5HNH-15 Dryer Based on BP Neural Network

作　　者：钟嘉豪 李长友[1] 黄嘉禧 黎斌 李成杰 张雪峰 Zhong Jiahao;Li Changyou;Huang Jiaxi;Li Bin;Li Chengjie;Zhang Xuefeng(College of Engineering,South China Agricultural University,Guangzhou 510642,China)

机构地区：[1]华南农业大学工程学院,广州510642

出　　处：《农机化研究》2023年第4期1-7,14,共8页Journal of Agricultural Mechanization Research

基　　金：国家自然科学基金项目(31671783,31371871)。

摘　　要：精确的工业化粮食干燥过程数学模型是实现其过程动态跟踪、闭环控制的前提。为此,基于5HNH-15连续式粮食干燥机,构建了8-11-1的BP神经网络预测模型,模型的输入为5HNH-15连续式干燥机的8个干燥影响因素,输出为出口粮食含水率。利用MmatLab软件进行BP神经网络模型的建立及验证,结果表明:模型在67次迭代后,均方误差MSE达到2.8361e-6,绝对误差小于±0.1,平均绝对误差MAE=0.0288,相对误差小于1.2%,回归系数R=0.99996,决定系数R 2=0.9998。新增1组验证试验,结果显示:模型预测值与实际值的绝对误差小于±0.1,平均绝对误差MAE=0.0121,相对误差小于1.1%,证明了所构建模型的精确性与普适性,可为实现工业化粮食干燥的智能控制提供理论依据和技术支撑。The precise mathematical model of the industrialized grain drying process is the prerequisite for realizing the dynamic tracking and closed-loop control of the process.Based on the 5HNH-15 continuous grain dryer designed by this laboratory,an 8-11-1 BP neural network prediction model is constructed.The input of the model is the 8 drying influencing factors of the 5HNH-15 continuous dryer,and the output of the model It is the moisture content of the exported grain.Using matlab software to establish and verify the BP neural network model,the results show that:after 67 iterations,the mean square error MSE of the constructed BP neural network model reaches 2.8361e-6,the absolute error is less than±0.1,and the average absolute error MAE=0.0288,the relative error is less than 1.2%,the regression coefficient R=0.99996,and the coefficient of determination R 2=0.9998.A new set of verification tests was added,and the results showed that the absolute error between the predicted value of the model and the actual value was less than±0.1,the average absolute error was MAE=0.0121,and the relative error was less than 1.1%.The intelligent control of industrialized grain drying provides theoretical basis and technical support.

关 键 词：干燥机 出机粮含水率 预测模型 BP神经网络 

分 类 号：S226.6[农业科学—农业机械化工程] S375[农业科学—农业工程]