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罗艺, 张韬, 李晓松. 基于模糊C均值聚类算法的模糊时间序列分析在戊肝发病率预测中的应用初探[J]. koko体育app 学报（医学版）, 2016, 47(3): 406-410.
引用本文: 罗艺, 张韬, 李晓松. 立于损坏C平均值聚类讲解贝叶斯的损坏耗时字段讲解在戊肝致病率预测分析中的软件应用探析[J]. 上海高中学报（临床医学版）, 2016, 47(3): 406-410.
LUO Yi, ZHANG Tao, LI Xiao-song. Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis[J]. Journal of Sichuan University (Medical Sciences), 2016, 47(3): 406-410.
Citation: LUO Yi, ZHANG Tao, LI Xiao-song. Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis[J]. Journal of Sichuan University (Medical Sciences), 2016, 47(3): 406-410.

基于模糊C均值聚类算法的模糊时间序列分析在戊肝发病率预测中的应用初探

Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis

  • 摘要: 目的 探讨基于模糊C均值聚类（fuzzy c-means clustering，FCM）算法的模糊时间序列分析在戊肝发病率预测中的应用价值。方法 采用基于FCM算法的模糊时间序列分析方法，对2004年1月至2014年7月我国内地法定报告的戊肝逐月发病率资料建立预测模型，并对2014年8 ～12月的相应数据进行预测，并将预测结果与经典模糊时间模型预测结果进行比较。结果 基于FCM算法的模糊时间序列模型的拟合均方误差（MSE）为0.001 1，预测MSE为6.977 5×10-4；而经典模糊时间序列模型的两个MSE分别为0.001 7和0.001 4。可见，基于FCM算法的模糊时间序列分析相对于经典模型，有较好的预测能力。结论 基于FCM算法的模糊时间序列分析对于戊肝等传染病发病率的预测具有较好的应用价值。  
    Abstract: Objective To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China． Methods Apredictive model (fuzzy time series method based on fuzzy c-means clustering)was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models． Results The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5×10-4MSEof forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. Conclusion The results indicatethat the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E．  
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