【摘要】：在流感暴發(fā)趨勢預測模型的研究中,傳統(tǒng)點預測是估計預測平均值的隨機變量,不包含置信水平和預測區(qū)間范圍等輔助決策的有用信息,導致決策者不能很好把握流感發(fā)展趨勢。為了解決上述問題,提出利用神經(jīng)網(wǎng)絡上下限估計方法(LUBE)建立預測區(qū)間(PI)發(fā)展了流感趨勢區(qū)間預測模型,提出了評價預測區(qū)間的寬度范圍組合指標(CWC),運用蟻群算法對神經(jīng)網(wǎng)絡區(qū)間預測模型進行訓練,并運用上述模型對傳染病等應急醫(yī)療數(shù)據(jù)進行了仿真。為了衡量預測區(qū)間性能,改進模型與Delta、Bayesian、Holt指數(shù)平滑和支持向量機等常用預測模型建立的預測區(qū)間進行了對比。結(jié)果表明蟻群算法神經(jīng)網(wǎng)絡區(qū)間預測模型能夠?qū)α鞲汹厔葸M行更為有效的分析和預測。
[Abstract]:In the research of influenza outbreak trend prediction model, the traditional point forecast is a random variable to estimate the average value of the forecast, and it does not contain useful information for auxiliary decision, such as confidence level and range of prediction interval, etc. As a result, policy makers can not grasp the trend of influenza. In order to solve the above problems, a prediction interval (PI) model is developed by using the upper and lower bound estimation method of neural network (LUBE) to develop the interval forecasting model of influenza trend. The combined index (CWC), of the width range for evaluating the prediction interval is proposed. Ant colony algorithm is used to train the interval prediction model of neural network, and the above model is used to simulate the emergency medical data such as infectious diseases. In order to evaluate the performance of prediction interval, the improved model is compared with the prediction interval established by Delta,Bayesian,Holt exponential smoothing and support vector machine. The results show that the interval prediction model of ant colony algorithm (ACA) neural network can effectively analyze and predict the trend of influenza.
【作者單位】： 上海交通大學機械動力工程學院;
【分類號】：R181.3;TP18

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