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基于組合核函數(shù)的秈稻重度不宜存檢測模型
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國家重大科技專項資助項目(4005-09500216),、中央高?;究蒲袠I(yè)務費專項資助項目(2011PY038)和華中農業(yè)大學科技創(chuàng)新基金(SRF)資助項目(2012029)


Identification Model of Severely Unstorable Indica Paddy Based on Combined Kernel Function
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    摘要:

    為給秈稻儲存品質的判定提供一種快速無損檢測手段,,對80份重度不宜存秈稻和80份非重度不宜存秈稻的近紅外反射光譜進行了實驗研究,。根據(jù)訓練樣本非線性可分的特點,選擇支持向量機方法建立定性模型,。在對不同核函數(shù)的特性進行分析和研究的基礎上,,定義了一種新的核函數(shù)——組合核函數(shù)。該組合核函數(shù)是多項式核函數(shù)與徑向基核函數(shù)的線性組合,,將兩者各自的特點融合在一起兼具內推和外推性能,。實驗結果表明,以這兩種函數(shù)的線性組合作為核函數(shù)且調節(jié)因子為0.7時,,所建立的模型綜合性能最好,。所建模型的訓練集正確識別率為97.21%,測試集正確識別率為93.25%,。

    Abstract:

    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%.

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石禮娟,謝彪彪,謝新港,吳洋.基于組合核函數(shù)的秈稻重度不宜存檢測模型[J].農業(yè)機械學報,2013,44(8):165-168,194. Shi Lijuan, Xie Biaobiao, Xie Xin’gang, Wu Yang. Identification Model of Severely Unstorable Indica Paddy Based on Combined Kernel Function[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(8):165-168,194.

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  • 在線發(fā)布日期: 2013-07-19
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