基于LIBSVM的粮食水分数据融合研究

通过建立粮食水分仪数学模型,获取其多输入参量与水分的关系曲线是提高水分测量精度的有效措施.以电容式水分仪为实验对象,在大量实验数据的基础上,利用LIBSVM中的径向基核函数回归找出其关系曲线.实验结果表明,所建立的模型能精确反应电容传感器、环境温度、粮食紧实度、重量等参量与水分之间规律和趋势,对于便携手持式水分仪的研究具有指导意义.

Research on Grain Moisture Detection Data Fusion based on LIBSVM

Through the establishment of a grain moisture meter mathematical model to obtain the relationship of the multi-input parameters and moisture to improve measurement accuracy curve is an effective measure. In this paper, Use capacitive moisture meter as experimental subjects, in a large number of experimental data, based on the use of LIBSVM the radial basis kernel regression to find out the curve. Experimental results show that the model can accurately reflect the patterns and trends of the capacitance sensor, ambient temperature, grain tightness, weight and other parameters with moisture, for the study of portable hand-held moisture meter is instructive