發(fā)布時(shí)間：2019-01-01 08:14

【摘要】：在流感暴發(fā)趨勢(shì)預(yù)測(cè)模型的研究中,傳統(tǒng)點(diǎn)預(yù)測(cè)是估計(jì)預(yù)測(cè)平均值的隨機(jī)變量,不包含置信水平和預(yù)測(cè)區(qū)間范圍等輔助決策的有用信息,導(dǎo)致決策者不能很好把握流感發(fā)展趨勢(shì)。為了解決上述問題,提出利用神經(jīng)網(wǎng)絡(luò)上下限估計(jì)方法(LUBE)建立預(yù)測(cè)區(qū)間(PI)發(fā)展了流感趨勢(shì)區(qū)間預(yù)測(cè)模型,提出了評(píng)價(jià)預(yù)測(cè)區(qū)間的寬度范圍組合指標(biāo)(CWC),運(yùn)用蟻群算法對(duì)神經(jīng)網(wǎng)絡(luò)區(qū)間預(yù)測(cè)模型進(jìn)行訓(xùn)練,并運(yùn)用上述模型對(duì)傳染病等應(yīng)急醫(yī)療數(shù)據(jù)進(jìn)行了仿真。為了衡量預(yù)測(cè)區(qū)間性能,改進(jìn)模型與Delta、Bayesian、Holt指數(shù)平滑和支持向量機(jī)等常用預(yù)測(cè)模型建立的預(yù)測(cè)區(qū)間進(jìn)行了對(duì)比。結(jié)果表明蟻群算法神經(jīng)網(wǎng)絡(luò)區(qū)間預(yù)測(cè)模型能夠?qū)α鞲汹厔?shì)進(jìn)行更為有效的分析和預(yù)測(cè)。
[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(xué)機(jī)械動(dòng)力工程學(xué)院;
【分類號(hào)】：R181.3;TP18

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