欢迎访问《中国农学通报》，
中国农学通报

中国农学通报 ›› 2013, Vol. 29 ›› Issue (33): 228-232.doi: 10.11924/j.issn.1000-6850.2012-3441

所属专题： 生物技术； 小麦

• 农学 农业基础科学 • 上一篇    下一篇

基于遗传算法的BP神经网络在小麦蛋白质含量预测中的应用

毛晓东 孙来军 徐璐璐   

  • 收稿日期:2012-10-19 修回日期:2012-11-20 出版日期:2013-11-25 发布日期:2013-11-25

Application of Predicting the Protein Content of wheat Based on BP Neural Network Model by Genetic Algorithm

  • Received:2012-10-19 Revised:2012-11-20 Online:2013-11-25 Published:2013-11-25

PDF (PC)

424

摘要： 为了快速、简便、准确地测定小麦蛋白质的含量，本文提出了应用近红外光谱分析技术结合遗传算法(GA)的BP神经网络的建模方法。采用SPXY算法对光谱数据进行了合理划分，并运用连续投影算法(SPA)将预处理过的数据压缩，对光谱数据提取最佳敏感波点作为GA-BP神经网络的输入，建立小麦蛋白质含量的校正模型。模型的预测均方根误差和预测相关系数为1.3379和0.979，并与BP神经网络所建立的校正模型进行了比较。结果表明：GA-BP神经网络所建模型收敛速度快、训练时间短、准确度也较高，能够实现对小麦蛋白质含量快速高效的检测。

关键词: 均衡机制, 均衡机制

Abstract: In order to be fast, simple and accurate determination of the protein content of wheat, this paper puts forward modeling method of near infrared spectral analysis technology combined with BP neural network using genetic algorithm (GA). Spectral data were rationally divided by SPXY algorithm, and making use of successive projections algorithm (SPA) compressed preprocessed data and extracted the best sensitive wave points as the input of GA-BP neural network to establish the calibration model of the protein content of wheat. Root-Mean-Square Error of Prediction (RMSEP) and prediction correlation coefficient (R) of the model were 1.3379 and 0.979 and compared with the correction model of BP neural network. The result showed that: GA-BP neural network model has fast speed of convergence, short training time and high accuracy, which is able to achieve rapid and efficient detection on the protein content of wheat.