吴宁,陈天恩,姜舒文,张驰,鲁梦瑶,张玮.基于灰色关联和PSO–SVM的葡萄霜霉病的短期预测[J].湖南农业大学学报:自然科学版,2020,46(2):.
基于灰色关联和PSO–SVM的葡萄霜霉病的短期预测
  
DOI:
中文关键词:  葡萄霜霉病  短期预测  支持向量机  粒子群算法  灰色关联分析  改进网格搜索法
英文关键词:grape downy mildew  short-term prediction  support vector machine  particle swarm optimization  grey relational analysis  improved grid search method
基金项目:北京市科委项目(Z171100001517005);北京市农林科学院青年科研基金项目(QNJJ201718)
作者单位
吴宁,陈天恩,姜舒文,张驰,鲁梦瑶,张玮 1.上海海洋大学信息学院上海 2013062.国家农业信息化工程技术研究中心北京 1000973.北京市农林科学院植物保护环境保护研究所北京 100097 
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中文摘要:
      运用灰色关联分析法(GRA)筛选出北京市房山地区的主要气象因子,作为支持向量机(SVM)模型的输入特征向量,通过粒子群算法(PSO)优化SVM的惩罚因子C和核函数参数δ,建立了基于灰色关联和PSO–SVM的葡萄霜霉病短期预测模型,应用该模型对该地区未来1 d的葡萄霜霉病发病等级进行短期预测。与改进网格搜索法优化的SVM模型、经验选择参数的标准SVM、不同训练函数和粒子群算法优化的BP网络模型进行比较,结果基于灰色关联分析的PSO–SVM模型预测效果最好,对葡萄霜霉病发病等级的预测正确率为95.24%,与基于全部气象因子的PSO–SVM模型相比,预测正确率提高了1.19%,运行速度快1.81 s。
英文摘要:
      The main meteorological factors in Fangshan district of Beijing were selected as the input feature vectors of the support vector machine(SVM) model by grey relational analysis. The particle swarm optimization algorithm(PSO) is used to optimize the penalty vector C and the kernel function parameter δ of SVM. The short-term prediction model of grape downy mildew was established based on grey correlation and PSO-SVM. The model was applied to predict the grape downy mildew in the next day. Compared with the SVM model optimized by improved grid search method, the standard SVM with experience selection parameters and the BP network model optimized by different training functions and particle swarm optimization, the PSO-SVM model based on grey correlation analysis has the best prediction effect with the accuracy of 95.24% for the incidence of grape downy mildew. Compared with the PSO-SVM model based on all meteorological factors, the accuracy is increased by 1.19%, and the operation speed is 1.81 s faster.
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