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中国农学通报

中国农学通报 ›› 2010, Vol. 26 ›› Issue (12): 238-241.

所属专题： 水稻

• 植物保护 农药 • 上一篇    下一篇

灰色人工神经网络在稻瘟病发生预报中的应用

张雷1,燕亚菲1,刘志红2,向卫国1   

  • 收稿日期:2010-02-02 修回日期:2010-02-21 出版日期:2010-06-20 发布日期:2010-06-20
  • 基金资助:

    四川省教育厅重点项目

Application of Grey Artificial Neural Network Analysis on Forecasting Epidemic of the Rice Blast

Zhang Lei1, Yan Yafei1, Liu Zhihong2, Xiang Weiguo1   

  • Received:2010-02-02 Revised:2010-02-21 Online:2010-06-20 Published:2010-06-20

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摘要：

摘要：针对稻瘟病在水稻生长过程中存在的严重危害，笔者基于四川资中地区1998-2008年的稻瘟病发生资料，运用灰色人工神经网络的方法（GBP ），建立了稻瘟病发生的预报模型，结果表明：灰色人工神经网络模型的平均相对误差为0.0946，远远优于GM(1,1)模型的1.8857。灰色人工神经网络模型可以拟合任意一种函数关系，且该模型信息利用率高，避免了系统数据辨识方法在序列累加时因正负抵消而产生信息失真的现象。灰色人工神经网络模型的拟合和预测精度较高，可以用于该地区稻瘟病发生的预测工作。

关键词: 小麦, 小麦, 栽培技术, 品质

Abstract:

Abstract: This article in view of the rice blast to rice growth process, by using the rice blast information of Zizhong, Sichuan province from 1998 to 2008, a rice blast prediction model is built based on gray artificial neural network. The model’s result shows that: the prediction accuracy of gray artificial neural network model is 0.0946, which is better than that of GM(1,1) model which is 1.8857. Gray artificial neural network model is feasible to fit all kinds of functional relationship, what’s more, it can make full use of the information and avoid the information distortion caused by positive and negative cancellation in the process of accumulative sequence of system identification. Gray artificial neural network model has good effects on fitting and forecasting precision, so the gray artificial neural network model can be used in the forecasting work of the occurrence of rice blast.