摘要
为给籼稻储存品质的判定提供一种快速无损检测手段,对80份重度不宜存籼稻和80份非重度不宜存籼稻的近红外反射光谱进行了实验研究。根据训练样本非线性可分的特点,选择支持向量机方法建立定性模型。在对不同核函数的特性进行分析和研究的基础上,定义了一种新的核函数——组合核函数。该组合核函数是多项式核函数与径向基核函数的线性组合,将两者各自的特点融合在一起兼具内推和外推性能。实验结果表明,以这两种函数的线性组合作为核函数且调节因子为0.7时,所建立的模型综合性能最好。所建模型的训练集正确识别率为97.21%,测试集正确识别率为93.25%。
In order to explore a rapid and non-destructive method for identifying storable quality of indica paddy,experiments were conducted on 80 samples of severely unstorable indica paddy and 80 samples of other indica paddy.Support vector machine（SVM） method was selected to build qualitative model according to training samples’characteristics.A combined kernel function was defined after analyzing and studying different kernel function.The proposed combined kernel function was the linear combination of polynomial function and radial basis function,and it combined each advantage of these two functions to acquire both interpolation and extrapolation properties of kernel function.The experiments showed that the model had the best comprehensive performance when the linear combination polynomial function and radial basis function was taken as the kernel function of SVM,and the control factor was 0.7.The training identification rate was 97.21%,and the test identification rate was 93.25%.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2013年第8期165-168,194,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重大科技专项资助项目(4005-09500216)
中央高校基本科研业务费专项资助项目(2011PY038)
华中农业大学科技创新基金(SRF)资助项目(2012029)
关键词
籼稻
近红外光谱
组合核函数
定性模型
支持向量机
Indica paddy NIR spectroscopy Combined kernel function Qualitative model Support vector machine
