The modeling research of wheat classification based on NIR and RBF neural network

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外文摘要：Since the traditional detection method of wheat quality was tedious and time-consuming， near infrared reflectance spectroscopy (NIRS) combined with RBF artificial neural network was used to classification and detection wheat quality non-destructively and quickly in this paper。 The ware point of samples obtained by the NIRS is too many， resulting in the structure of RBF neural network is too complex， so we used the algorithm of radial basis function (PSO) to optimize RBF neural network， and made some improvement measures against the shortcoming of premature convergence and the set of inertia weight was too mechanical of PSO algorithm。 The experimental analysis showed that the accuracy of model can reached 98%， which could satisfy the need of non-destructive and real-time detection of wheat in modern agriculture。

外文关键词：

agriculturecropsneural netsnondestructive testingparticle swarm optimisationradial basis function networksspectroscopyNIRPSO algorithmRBF artificial neural networkRBF neural networknear infrared reflectance spectroscopyradial basis functionwheat classificationwheat quality nondestructive classificationwheat quality nondestructive detectionAlgorithm design and analysisCalibrationClustering algorithmsNeural networksOptimizationSpectroscopyTrainingPSO algorithmRBF neural networkclassification modelwheat quality

作者：

Hui Zheng、Laijun Sun、Guangyan Hui、Xiaodong Mao、Shang Gao

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作者单位：

Heilongjiang Univ., Harbin, China

会议名称：

International Conference on Computer Science and Network Technology

会议地点：

Dalian(CN)

会议母体文献：

2013 3rd International Conference on Computer Science and Network Technology

页码：

1122-1127

出版时间：

2013

DOI：

10.1109/ICCSNT.2013.6967300