中国农业气象

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BP神经网络模型在广西原料蔗产量预报中的应用

欧钊荣;谭宗琨;何燕;丁美花;杨鑫;   

  1. 广西壮族自治区气象减灾研究所,广西壮族自治区气象减灾研究所,广西壮族自治区气象减灾研究所,广西壮族自治区气象减灾研究所,广西壮族自治区气象减灾研究所 广西南宁530022,广西南宁530022,广西南宁530022,广西南宁530022,广西南宁530022
  • 出版日期:2008-04-10 发布日期:2008-04-10
  • 基金资助:
    中国气象局新技术推广项目(CMATG2006M42)

Application of BP Neural Network in Yield Predication of Sugarcane(Saccharum officinarum L.) in Guangxi Province

OU Zhao-rong,TAN Zong-kun,HE Yan,DING Mei-hua,YANG Xin(Guangxi Research Institute of Meteorological Disasters Mitigation,Nanning 530022,China)   

  • Online:2008-04-10 Published:2008-04-10

摘要: 将广西甘蔗种植区气象台站的光温水资料采用膨化处理后按旬依次组合成不同时段值,通过相关分析方法普查选出与广西原料蔗产量相关系数通过0.01水平显著性检验的因子作为预报因子,分别用逐步回归方法和误差反传前向网络(BP神经网络)建立广西原料蔗产量预报模型,模拟结果对比分析显示,BP神经网络预报模型的拟合精度和预报精度均高于逐步回归模型。

关键词: BP神经网络, 逐步回归, 甘蔗, 产量预报

Abstract: The data of the light,temperature and precipitation in the growing area of sugarcane(Saccharum officinarum L.) in Guangxi Autonomous Region was combined to the data with different period of the time by each ten days by using the expansion technique.The predicted factors with the correlative coefficient at 0.01 level of the significance were selected by using the method of correlative pervasive investigation.The regression model was established by the stepwise regression to predict the sugarcane yields.The prediction model of BP Neural Network was established by using the same factors of the stepwise regression at the same time.The precision of prediction and simulation results by the model of BP Neural Network was higher than that by the stepwise regression model.

Key words: BP Neural Network, BP Neural Network, Stepwise regression, Sugarcane(Saccharum officinarum L.), Yield prediction