異常數(shù)據(jù)的診斷、處理及化工應(yīng)用

化工論文編號(hào):HG178  論文字?jǐn)?shù):12365,頁數(shù):32

 目  錄
中文摘要 I
英文摘要 II
目錄 III
1. 緒論 1
 1.1  化工測(cè)試中數(shù)據(jù)質(zhì)量保證的意義 1
 1.2  異常數(shù)據(jù)的來源 2
 1.3  異常數(shù)據(jù)的特征分析 2
 1.4  目前常用診斷和處理異常數(shù)據(jù)方法 3
 1.4.1  物理判別法 3
 1.4.2  基于統(tǒng)計(jì)的方法 3
 1.4.3  基于距離的方法 4
 1.4.4  基于偏差的方法 6
 1.4.5  基于密度的方法 6
 1.4.6  各種方法優(yōu)缺點(diǎn)小結(jié) 6
2. 實(shí)驗(yàn)部分 8
 2.1  多元線性回歸分析 10
 2.1.1  多元線性回歸模型 10
 2.1.2  多元線性回歸模型的檢驗(yàn) 10
 2.1.3  實(shí)例應(yīng)用 11
 2.2  多元逐步回歸分析 13
 2.2.1  原理介紹 13
 2.2.2  多元逐步回歸分析的步驟 13
 2.2.3  實(shí)例應(yīng)用 14
 2.3  多元逐步回歸分析 16
 2.3.1  多項(xiàng)式回歸模型介紹 16
 2.3.2  Matlab 程序?qū)崿F(xiàn) 17
3. 結(jié)果與討論 20
 3.1   多元線性回歸方法的結(jié)果分析 20
 3.2  多元逐步回歸分析的結(jié)果分析 22
 3.3  三種方法結(jié)合起來的優(yōu)勢(shì) 25
4．總結(jié)與展望 38
致謝 30
參考文獻(xiàn) 31

摘 要：在化工數(shù)據(jù)測(cè)試中，數(shù)據(jù)質(zhì)量的保證有著很大的意義，對(duì)于異常數(shù)據(jù)需要準(zhǔn)確診斷和處理。本文列舉了目前常見的幾種化工異常數(shù)據(jù)診斷和處理的方法，分析了它們的基本原理和算法步驟，并歸結(jié)了各自的優(yōu)缺點(diǎn)。而后，以飛機(jī)的剩余油量觀測(cè)數(shù)據(jù)為例，探討基于統(tǒng)計(jì)的方法如何來處理該類問題。首先用一般的多元回歸方法來處理數(shù)據(jù)，發(fā)現(xiàn)這樣處理出來的數(shù)據(jù)線性相關(guān)系數(shù)很小，于是，做了一些改進(jìn)，采用逐步回歸分析方法。逐步回歸分析可以將數(shù)據(jù)中離群顯著的值去掉，然后再對(duì)剩余的值做回歸處理；發(fā)現(xiàn)這樣處理的數(shù)據(jù)線性相關(guān)明顯改善。但因這些都是基于預(yù)先設(shè)定數(shù)據(jù)本身就具有線性的基礎(chǔ)上得出的結(jié)論，于是本文再次對(duì)數(shù)據(jù)處理方法做進(jìn)一步改進(jìn)，引入了多項(xiàng)式回歸方法來處理。這樣處理的出來的數(shù)據(jù)明顯有很好的線性相關(guān)，效果也比較好。

關(guān)鍵詞：異常數(shù)據(jù)；延遲焦化；線性回歸分析；多元逐步回歸；多項(xiàng)式回歸

Abstract: In chemical data test, the guarantee of data quality has great meanings, for unusual data needs accurate diagnosis and handling. This paper has enumerated the present common methods of some kinds of chemical unusual data diagnosis and handling. It analysed their basic principle and algorithm steps , and summed up the good and shortcomings of each one. Then, with the surplus oil of airplane as an example to measure observation data, we discusssed the method based on statistics how to come to handle this kind of problem. First, as handling data with general multivariate regression method, we discover such handling the data linear correlation coefficient that comes out very little. So, to make some improvements , we adopt step by step regression analysis method. Regress analysis step by step can will data in is away from crowd notable value remove , then again the value for surplus do regression handling; Discover the data of such handling linear related obvious improvement. But because these are all based on setting data in advance having the conclusion that reached on linear foundation, so this paper do improve further again for data processing technique. It have led into polynomial regression method to handle. So handling comes out that data have obviously very good relating linear, and the effect is also compared.

 Keywords：unusual data; delayed coking; linear regression analysis；multivariate stepwise regression；polynomial regression