基于BP神经网络方法的高大平房仓温度场预测研究

国家粮食储备多以平房仓为主要存储仓型,储粮粮堆在夏季时受到外界持续传热而会达到较高温度,而且微生物生长繁殖会进一步引起粮堆内部发热,对安全储粮产生危害。为了确保储粮品质,控制储粮温度,粮仓温度场预测系统的研究与应用就愈显重要。基于神经网络模型,以BP神经网络预测模型为主要研究对象,并选取典型高大平房仓实际粮情监测数据为实例,在MATLAB平台进行仿真,通过实测数据进行训练,构建实测模型。分析了粮食温度场的影响因素,采用SPSS统计学软件确定了影响因素的权重大小,并采用神经网络方法验证了主成分分析的结果。

Prediction of the temperature field of high & large warehouse based on BP neural network

Large warehouse is the main type of grain granary for state grain depot. In the summer, due to the continuous heat outside, grain pile will reach a higher temperature, moreover, the growth of microorganisms will lead to further internal heat for grain heap, which would harm the security of stored grain. In order to safeguard the quality of the stored grain and control the temperature, it is important to research and apply granary temperature field prediction system. BP neural network forecasting model was studied based on neural network model. The actual monitoring data of grain in warehouse were selected to emulate on MATLAB platform, and construct models. The factors which affected the grain temperature field were analyzed, and the weights of the factors were determined by SPSS statistical software, the results of the principal component analysis were verified by neural network method.