臺灣博碩士論文加值系統

摘要

在統計製程管制中，當製程存在可歸屬原因時，管制圖上會呈現特定的非隨機性樣式的變化。正確辨識非隨機性樣式，可以縮小診斷製程異常原因之範圍而有助於規劃改善方案。本研究目的是發展一個可有效辨識趨勢、週期性及平均值偏移等非隨機性樣式種類及正常數據之辨識系統。

本研究提出一種以類神經網路為基礎之辨識程序，做為管制圖非隨機性樣式之辨識系統。在網路輸入向量的設定上分別以 (1) 原始數據及 (2) 結合小波理論之數據前處理過程所得的特徵值兩種方法進行實驗比較與探討。本文的主要貢獻為小波理論中之多重解析度分析過程所得之各階特徵係數因具有良好的能量集中性質，而有助於以較小的網路輸入向量描述各樣式的特徵及其差異性，減少執行運算及訓練的時間。

本研究所建立之辨識系統是以正確辨識率來評估其效益。經模擬法分析證明本研究所提出之小波特徵擷取方法確實可提高應用時之辨識能力，同時亦降低原有網路結構的複雜性。

ABSTRACT

In the statistical process control, control chart patterns recognition plays an important role. When the process existed may belong to the assignable causes, a control chart can present the specific non-random patterns. Correctly recognizes the non-random patterns may reduce examines scope of the regulation abnormal reason to be helpful to the improvement plan. The purpose of this research is to develop a system to recognize the trend, cycle, shift non-random patterns and the normal data effectively.

In this research, we proposed a neural network-based pattern recognizer for the analysis of control chart patterns. In addition, we have developed and compared with two kind of input vectors: raw data and features that extrascted from wavelet theory and multi-resolution analysis. The concepts of wavelet theory and multi-resolution analysis (MRA) have be used for data pre-processing. It seems more capable of detecting unnatural patterns as well as describing the key features of the specific pattern detected and the excuting time of artificial neural network.

Since the pattern recognizer of this research is appraised its benefit by the rate of correct classification. The results of simulation have show that the pattern recognizer using features based on wavelet theory and multi-resolution analysis can recognize the control chart patterns better than raw data and the structure of neural network is simply, too.

目錄
頁次
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 x

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 3
1.4 研究方法與步驟 5
1.5 論文架構 7
第二章 文獻探討 8
2.1 傳統統計製程管制法 8
2.2 管制圖非隨機樣式之辨識 9
2.3 以類神經網路偵測管制圖非隨機性樣式 13
2.4 結合小波分析於管制圖非隨機性樣式之偵測 17
第三章 小波轉換理論 25
3.1 小波理論的發展 25
3.2 小波理論的性質 28
3.3 小波基底的類別 29
3.3.1 連續小波轉換 29
3.3.2 離散小波轉換 31
3.4 多重訊號解析 32
第四章 管制圖非隨機樣式之辨識系統 40
4.1 非隨機樣式數據之模擬 43
4.2 辨識系統之架構 45
4.3 設定網路之分析視窗 47
第五章 實驗結果與評估 50
5.1 評估指標 50
5.2 評估程序 50
5.3 實驗結果評估及分析 50
5.3.1 測試方式Ⅰ 55
5.3.2 測試方式Ⅱ 57
第六章 結論及後續建議 65
參考文獻 67

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