本文選題：非線性預(yù)測(cè) + 神經(jīng)網(wǎng)絡(luò)��； 參考：《成都理工大學(xué)》2007年碩士論文

【摘要】： 本論文針對(duì)艾滋病療法效果的所得到的臨床數(shù)據(jù)(ACTG320)，采用了定量預(yù)測(cè)方法對(duì)數(shù)據(jù)進(jìn)行處理，尋找其內(nèi)在規(guī)律性，擬得出艾滋病治療方法對(duì)病人病情的影響，預(yù)測(cè)繼續(xù)治療的效果。 論文先介紹了論題的相關(guān)背景知識(shí)，包括常見的艾滋病療法及其效果，知道CD4數(shù)目和HIV濃度是衡量艾滋病患者病情的兩個(gè)重要指標(biāo)量，引出了本論文的主要任務(wù)就是用一些數(shù)學(xué)預(yù)測(cè)方法來模擬CD4和HIV之間非線性的關(guān)系。 為了實(shí)現(xiàn)對(duì)艾滋病療法效果的預(yù)測(cè)，論文以CD4數(shù)目初值、HIV濃度的初值和治療時(shí)間段為輸入項(xiàng)，CD4數(shù)目改變量、HIV濃度改變量為輸出項(xiàng)，建立了BP神經(jīng)網(wǎng)絡(luò)。通過對(duì)原始數(shù)據(jù)進(jìn)行分組和預(yù)處理，對(duì)網(wǎng)絡(luò)進(jìn)行訓(xùn)練和泛化能力研究，得到了合理的網(wǎng)絡(luò)模型，將其應(yīng)用于艾滋病療法的長(zhǎng)期治療效果預(yù)測(cè)。針對(duì)不同的病人，預(yù)測(cè)了其繼續(xù)治療的效果，對(duì)于效果不好的，給出了提前終止治療的時(shí)間。 其次，論文分別以CD4數(shù)目初值、HIV濃度的初值和治療時(shí)間段為輸入項(xiàng)，CD4數(shù)目改變量或HIV濃度改變量為輸出項(xiàng)建立了回歸支持向量機(jī)模型。通過對(duì)樣本的學(xué)習(xí)訓(xùn)練，將訓(xùn)練好的回歸支持向量機(jī)應(yīng)用于艾滋病療效的預(yù)測(cè)。 文中通過BP神經(jīng)網(wǎng)絡(luò)和回歸支持向量機(jī)兩種預(yù)測(cè)方法在艾滋病療法效果研究上的應(yīng)用，，經(jīng)過對(duì)比，發(fā)現(xiàn)基于支持向量機(jī)的艾滋病療法效果預(yù)測(cè)模型在估計(jì)精度、預(yù)測(cè)能力等方面都優(yōu)于BP神經(jīng)網(wǎng)絡(luò)。
[Abstract]:According to the clinical data of AIDS therapy, the quantitative prediction method is used to process the data, to find out the inherent regularity, to obtain the effect of AIDS treatment on the patient's condition, and to predict the effect of continuous treatment.The paper first introduces the relevant background knowledge of the topic, including the common AIDS treatment and its effect. We know that the number of CD4 and the concentration of HIV are two important indicators to measure the condition of AIDS patients.The main task of this paper is to simulate the nonlinear relationship between CD4 and HIV by some mathematical prediction methods.In order to predict the effect of AIDS therapy, a BP neural network was established by using the initial value of CD4 number and the initial value of CD4 concentration and the time period of treatment as input items, CD4 number change quantity and HIV concentration change quantity as output item.Through grouping and preprocessing the raw data and studying the network training and generalization ability, a reasonable network model is obtained, which is applied to predict the long-term therapeutic effect of AIDS therapy.For different patients, the effect of continuous treatment was predicted, and the time of early termination of treatment was given.Secondly, the regression support vector machine model was established using the initial value of CD4 number and the initial value of CD4 concentration as the input item, CD4 number change or HIV concentration change as the output item, respectively.The trained regression support vector machine (RSVM) was applied to predict the effect of AIDS.Through the application of BP neural network and regression support vector machine (RSVM) in the study of the effect of AIDS therapy, it is found that the prediction model based on support vector machine (SVM) is accurate in estimating the effect of AIDS therapy.The prediction ability is better than BP neural network.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2007
【分類號(hào)】：R512.91;R311

【引證文獻(xiàn)】

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1 周慎;基于SQL server2005的罪犯信息數(shù)據(jù)挖掘技術(shù)研究與應(yīng)用[D];電子科技大學(xué);2010年

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