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研究生:羅光佑
研究生(外文):Kuang-Yu Lo
論文名稱:應用類神經網路與支援向量機於線上即時辨識管制圖非隨機形狀之績效比較
論文名稱(外文):Comparison of Performance Using Artificial Neural Network and Support Vector Machine in On-line Control Chart Pattern Recognition
指導教授:顧瑞祥顧瑞祥引用關係
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:工業工程與管理研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:163
中文關鍵詞:類神經網路支援向量機統計製程管制管制圖統計特徵值
外文關鍵詞:artificial neural networksupport vector machinestatistical process controlcontrol chartstatistical characteristic value
相關次數:
  • 被引用被引用:2
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  • 收藏至我的研究室書目清單書目收藏:1
統計製程管制 (statistical process control, SPC) 是以統計手法針對製程進行監控、分析與改善,所以是屬事前預防。其主要是監控製程是否存在可歸屬原因 (assignable causes) 所導致之變異,以提早採取製程改善之行動,避免增加產品額外的生產成本。而在統計製程管制方法中管制圖 (control chart) 是最常被應用之重要工具,它可以用來決定系統的狀態並偵測製程中隨時可能發生的異常情況。異常的管制圖形狀與製程變異中一些特殊的非機遇性原因有關聯,因此有效地辨識異常管制圖形狀能減少可能需要的檢查次數,並加速診斷搜尋,所以這是屬於一種分類問題。而類神經網路 (artificial neural network) 與支援向量機 (support vector machine) 被廣泛的應用在分類問題上,並有許多研究指出具有良好的效益,因此本研究擬應用類神經網路與支援向量機做為線上偵測製程之監控系統,並利用蒙地卡羅模擬法產生出生產線製程數據並結合由製程數據所擷取出的統計特徵值當作此兩種工具的訓練範例與測試範例,並做其比較。本論文同時討論在常態環境下利用標準差代替系統或生產線上的雜訊值,所以利用不同標準差做動態測試的動作,並比較其績效。結果顯示,支援向量機的整體平均辨識率為 91% 優於類神經網路的整體平均辨識率 83%;支援向量機的整體平均連串長度為 6.5 優於類神經網路的整體平均連串長度 6.8;支援向量機的整體連串長度標準差為 1.8 優於類神經網路的整體連串長度標準差2.1。由結果證明,支援向量機在對於線上辨識異常管制圖的整體績效或者雜訊容忍程度是優於類神經網路的。
Statistical process control is a method of statistic to be aimed at manufacturing to proceed monitor, analysis and improve. So it is belong to preventing in advance. It can detect assignable cause during the process control which may occur and provide help to improve process and reduce unnecessary product cost. Hence, control chart is an important tool at statistical process control. Control charts can detect abnormal status during the process control which may occur at any time. Unnatural CCPs can be associated with a particular set of assignable causes for process variation. Essentially, the judgement of the process states can be seen as a classification problem in artificial intelligence. Artificial neural network (ANN) and support vector machine (SVM) generally were used in classification pattern, and a lot of researches were pointed out that ANN and SVM have excellent performances. So in this research is using ANN and SVM in on-line manufacturing control system. The training data set and testing data set were used in which hybrid training data were generated by Monte-Carlo Simulation Method for production line process data. At the same times to discuss under the normal environment to make use of standard deviation to replace system or production line’s noise, so make use of the action , that the different standard deviation does a dynamic state test and compare its results. The result showed that SVM’s overall average recognition rate is 91% surpass ANN’s overall average recognition rate 83% ; SVM’s overall average run length is 6.5 surpass ANN’s overall average run length 6.8 ; SVM’s overall sigma of run length is 1.8 surpass ANN’s overall sigma of run length 2.1 . Proved by the result that SVM’s overall performance or the noise tolerant of degree surpass ANN’s.
中文摘要 --------------------------------------------- i
英文摘要 --------------------------------------------- ii
誌謝 --------------------------------------------- iii
目錄 --------------------------------------------- iv
表目錄 --------------------------------------------- vii
圖目錄 --------------------------------------------- viii
符號說明 --------------------------------------------- xi
第一章 緒論----------------------------------------- 1
1.1. 研究背景與動機------------------------------- 1
1.2. 研究目的------------------------------------- 3
1.3. 研究範圍------------------------------------- 4
1.4. 研究假設------------------------------------- 5
1.5. 研究方法與流程------------------------------- 6
1.6. 論文架構------------------------------------- 10
第二章 文獻探討------------------------------------- 12
2.1. 傳統製程管制方法之相關文獻------------------- 12
2.2. 辨識非隨機性模型之相關文獻------------------- 15
2.3. 應用類神經網路於製程非隨機形狀辨識之相關文獻- 16
2.4. 應用支援向量機之相關文獻--------------------- 20
2.5. 擷取特徵值之相關文獻------------------------- 26
第三章 管制圖非隨機形狀----------------------------- 28
3.1. 管制圖非隨機形狀之種類----------------------- 28
3.2. 製程數據收集--------------------------------- 32
3.3. 數據之模擬----------------------------------- 33
3.3.1. 模擬的定義與程序----------------------------- 33
3.3.2. 蒙地卡羅模擬法的基本理論架構----------------- 35
3.4. 管制圖非隨機形狀之數學式--------------------- 36
3.5. 製程中非隨機形狀辨識程序所具備之條件--------- 38
第四章 類神經網路----------------------------------- 40
4.1. 類神經之發展過程----------------------------- 40
4.2. 類神經網路之基本原理------------------------- 42
4.3. 類神經網路組成要素--------------------------- 45
4.4. 類神經網路之特性與應用----------------------- 48
4.5. 倒傳遞網路----------------------------------- 50
4.6. 倒傳遞網路之學習演算法----------------------- 53
4.7. 倒傳遞類神經網路之參數設定------------------- 56
第五章 支援向量機----------------------------------- 59
5.1. 支援向量機之發展介紹------------------------- 59
5.2. 支援向量機簡介------------------------------- 59
5.3. 支援向量機的種類----------------------------- 62
5.4. 支援向量機之優點----------------------------- 69
5.5. 支援向量機之應用----------------------------- 69
第六章 統計特徵值----------------------------------- 72
6.1. 平均值--------------------------------------- 72
6.2. 標準差--------------------------------------- 72
6.3. 偏態係數------------------------------------- 73
6.4. 峰態係數------------------------------------- 73
6.5. 斜率----------------------------------------- 74
6.6. 皮爾森相關係數------------------------------- 74
第七章 辨識管制圖非隨機形狀之類神經網路------------- 75
7.1. 分析視窗------------------------------------- 75
7.2. 網路架構------------------------------------- 76
7.3. 產生訓練樣本--------------------------------- 80
7.4. 類神經網路模型之訓練------------------------- 81
7.5. 類神經網路模型之測試------------------------- 82
7.6. 衡量指標------------------------------------- 83
第八章 辨識管制圖非隨機形狀之支援向量機------------- 84
8.1. 研究架構------------------------------------- 84
8.2. 分析視窗------------------------------------- 88
8.3. 支援向量機之輸入卅輸出向量與訓練樣本之產生--- 88
8.4. 支援向量機之訓練----------------------------- 90
8.5. 支援向量機之測試----------------------------- 91
8.6. 衡量指標------------------------------------- 92
第九章 系統績效評估設定----------------------------- 93
9.1. 評估指標------------------------------------- 93
9.2. 類神經網路之評估程序------------------------- 93
9.3. 支援向量機之評估程序------------------------- 95
9.4. 動態測試與相關參數設定----------------------- 96
9.4.1. 類神經網路之動態測試相關參數設定------------- 97
9.4.2. 支援向量機之動態測試相關參數設定------------- 99
第十章 測試結果與分析------------------------------- 102
10.1. 類神經網路動態測試結果----------------------- 102
10.2. 支援向量機動態測試結果----------------------- 117
10.3. 類神經網路與支援向量機之績效評估與比較------- 132
第十一章 結論與未來展望------------------------------- 152
11.1. 結論----------------------------------------- 152
11.2. 貢獻----------------------------------------- 154
參考文獻 貢獻----------------------------------------- 156
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