【摘要】：目的建立用于河南省法定報告?zhèn)魅静?甲乙類)預(yù)測的神經(jīng)網(wǎng)絡(luò)模型,為制定傳染病預(yù)防和控制措施提供理論依據(jù)。方法首先確定預(yù)測模型的基本結(jié)構(gòu),以歸一化后的2003-2009年河南省甲乙類法定報告?zhèn)魅静“l(fā)病率數(shù)據(jù)為訓(xùn)練樣本,以2010年的數(shù)據(jù)為檢驗樣本,采用改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)算法訓(xùn)練預(yù)測模型。利用該模型對2011-2013年河南省甲乙類法定報告?zhèn)魅静“l(fā)病率數(shù)據(jù)進(jìn)行預(yù)測。結(jié)果所建立的模型在仿真預(yù)測樣本點的平均相對誤差為0.076%,在檢驗樣本處的預(yù)測誤差為0.434%。并獲得了2011-2013年河南省甲乙類法定報告?zhèn)魅静“l(fā)病率預(yù)測數(shù)據(jù)。結(jié)論所建立的BP神經(jīng)網(wǎng)絡(luò)模型具有良好的預(yù)測精度,適合用來進(jìn)行河南省甲乙類法定報告?zhèn)魅静“l(fā)病率的預(yù)測。
[Abstract]:Objective to establish a neural network model for predicting legally reported infectious diseases (class A and B) in Henan Province, and to provide theoretical basis for the prevention and control of infectious diseases. Methods first of all, the basic structure of the prediction model was determined, and the standardized incidence data of category A and B legally reported infectious diseases in Henan Province from 2003 to 2009 were taken as training samples, and the data of 2010 were taken as test samples. The improved BP neural network algorithm is used to train the prediction model. The model was used to predict the incidence data of Class A and B legally reported infectious diseases in Henan Province from 2011 to 2013. Results the average relative error of the model at the simulation prediction sample point is 0.076%, and the prediction error at the test sample is 0.434%. The forecast data of the incidence of Class A and B legally reported infectious diseases in Henan Province from 2011 to 2013 were obtained. Conclusion the BP neural network model has good prediction accuracy and is suitable for predicting the incidence of Class A and B legally reported infectious diseases in Henan Province.
【作者單位】： 河南中醫(yī)學(xué)院基礎(chǔ)醫(yī)學(xué)院;
【基金】：河南省軟科學(xué)研究重點項目(102400440002)
【分類號】：R183

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