計算機專業(yè)畢業(yè)設(shè)計

1、本文格式為Word版，下載可任意編輯 計算機專業(yè)畢業(yè)設(shè)計 計算機學院畢業(yè)論文 本科畢業(yè)設(shè)計（論文）BP神經(jīng)網(wǎng)絡(luò)的異常點檢測應(yīng)用可行性研究 學院計算機學院 專業(yè)軟件工程 年級班別 2022級（4）班 學號 3106007039 學生姓名蔡東赟 指導(dǎo)教師王麗娟 2022年 5 月 II III 摘要 異常點數(shù)據(jù)是指數(shù)據(jù)集中與眾不同數(shù)據(jù)。這部分數(shù)據(jù)的量小，但是對于我們的日常生產(chǎn)生活的影響極大。因此，異常點檢測被廣泛應(yīng)用于網(wǎng)絡(luò)入侵檢測，金融保險，天氣預(yù)報以及新藥研制等領(lǐng)域。相對于大量的正常數(shù)據(jù)挖掘而言，異常點檢測被稱作小模式數(shù)據(jù)挖掘。BP算法是一種常用的數(shù)據(jù)挖掘算法。但是BP算法進行實際數(shù)據(jù)的異常點數(shù)

2、據(jù)挖掘過程中存在：實際數(shù)據(jù)的維數(shù)較高，存在冗余特征的干擾，以及在高維特征下，數(shù)據(jù)量不充分的問題。因此，本文分析BP神經(jīng)網(wǎng)絡(luò)處理各種數(shù)據(jù)的狀況，并得到以下結(jié)果。（1）BP神經(jīng)網(wǎng)絡(luò)能夠較好的分開特征單一的仿真數(shù)據(jù)；但是（2）特征相像性較大的數(shù)據(jù)集，難以分開判斷；（3）正常數(shù)據(jù)不充分或者不具有代表性，因此正常數(shù)據(jù)類學習不充分，從而導(dǎo)致異常無法判斷。針對以上問題，本文提出了以下的提升措施：（1）BP算法前進行特征約簡（映射）從中選取有益于異常檢測的特征（2）多神經(jīng)網(wǎng)絡(luò)融合，不同神經(jīng)網(wǎng)絡(luò)識別不同的特征，相互取長補短，融合后得到最終的結(jié)果。 關(guān)鍵字：異常，BP，異常點檢測，神經(jīng)網(wǎng)絡(luò) 注：本設(shè)計（論文）題目

3、來源于教師的國家級（或部級、省級、廳級、市級、校級、企業(yè)）科研工程，工程編號為：。 IV V Abstract Outlier data is the data set different data. This part of the small amount of data, but for our daily production and life of great. Therefore, the anomaly detection is widely used in network intrusion detection, finance, insurance, weather, and

4、 new drug development and other fields. Relative to the large number of normal data mining, the anomaly detection model is called data mining small. BP algorithm is a commonly used data mining algorithm. But the BP algorithm to real data outliers exist in the data mining process: the higher the dime

5、nsion of the actual data, there are redundant features of the interference, and high-dimensional feature, the issue of inadequate data. Therefore, this paper analyzes a variety of BP neural network processing of data, and to get the following results. (1) BP neural network can better separation char

6、acteristics of a single simulation data; but (2) the characteristics of similar large data sets, separation is difficult to judge; (3) normal data is not sufficient or not representative, so the normal data class learning is not sufficient, leading to abnormal can not judge. To solve the above probl

7、em, this paper proposes the following improvements: (1) BP algorithm before feature reduction (map) benefit from anomaly detection features selected (2) integration of multiple neural networks, different neural network to recognize the different characteristics of each each other, the final fusion r

8、esult. Key Words：Outliers-Data，BP，Algorithms，Neural Networks VI VII 目錄 1引言 (1) 1.1背景 (1) 1.2傳統(tǒng)已有異常點算法介紹 (1) 1.2.1基于統(tǒng)計學的異常點檢測算法 (1) 1.2.2基于距離的異常點檢測算法 (2) 1.2.3基于密度的算法 (3) 1.2.4基于偏差的異常點檢測 (5) 1.2.5基于聚類的異常點檢測算法 (6) 2基于屬性特征在異常點檢測中的研究 (7) 3 BP神經(jīng)網(wǎng)絡(luò)介紹 (9) 3.1模型簡介 (9) 3.2計算各層節(jié)點輸出 (9) 3.3 修正權(quán)值 (10) 4 異常檢測中BP

9、神經(jīng)網(wǎng)絡(luò)的設(shè)計 (13) 4.1可微閾值單元 (13) 4.2單個BP網(wǎng)絡(luò)結(jié)構(gòu)設(shè)計 (13) 4.3BP神經(jīng)網(wǎng)絡(luò)學習過程的基本步驟 (14) 5試驗研究 (17) 5.1研究使用的數(shù)據(jù)庫介紹 (17) 5.2訓(xùn)練方案一試驗：把bp神經(jīng)網(wǎng)絡(luò)相像性代替距離算法相像度量 (17) 5.3訓(xùn)練方案二試驗：用單個神經(jīng)網(wǎng)絡(luò)對訓(xùn)練數(shù)據(jù)庫整體特性進行學習 (18) 5.4訓(xùn)練方案三試驗：多神經(jīng)網(wǎng)絡(luò)各種形式訓(xùn)練及其決策 (19) 5.4.1試驗設(shè)計思路 (19) 5.4.2試驗方案及步驟 (20) 5.4.3試驗分析 (22) 5.4.4試驗失敗理由分析 (23) 5.5BP調(diào)參試驗 (25) 5.5.1對試驗一調(diào)整隱層試驗 (25) 5.5.2對試驗二調(diào)整隱層試驗