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小麦赤霉病CBR预测模型参数的优化
关东,陈莉,张沙沙,丁克坚,朱诚,刘家成,郑朝阳
0
(安徽农业大学植物保护学院,合肥 230036;安徽省农业委员会,合肥 230061)
摘要:
基于类比推理人工智能方法CBR原理建立了以时间序列为轴线的小麦赤霉病滚动预测模型。为提高模型预测的准确性,利用安徽省农作物病虫数据库,结合专家经验会商的结果,优化筛选该预测模型中的预测单元、预测阶段、各预测阶段的权重、气象因子及其权重等各关键预测参数,并检验其预测的准确性。结果表明,运用优化后的参数,小麦赤霉病CBR预测模型常年预测准确率可达84.21%。故优化后的预测参数可用于小麦赤霉病CBR预测模型。
关键词:  案例推理  预测因子  权重  优化
DOI:
基金项目:公益性行业(农业)科研专项(201203016)资助。
Parameter optimization for CBR model of wheat scab
GUAN Dong,CHEN Li,ZHANG Shasha,DING Kejian,ZHU Cheng,LIU Jiacheng,ZHENG Zhaoyang
(School of Plant Protection, Anhui Agricultural University, Hefei 230036;Agriculture Committee in Anhui Province, Hefei 230061)
Abstract:
A rolling forecasting model of wheat scab was set up using CBR theory with time series. To improve the accuracy and reliability of this model, impact factors and parameters, such as model unit, prediction stages, meteorological factors and its weight were optimized by using the database system of crops pests in Anhui province and the results of expert consultation. The accuracy of CBR model of wheat scab with optimized parameters was tested, and the results showed that the precision rate was 84.21% in a normal year. The optimized factors and parameters could be applied to the forecasting model of wheat scab.
Key words:  case-based reasoning  impact factors  weight  optimization

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