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研究生： 張峯銘
Chang, Fengming
論文名稱： 使用總合模糊法以改善小資料學習之準確度於彈性製造系統之排程
Using Mega-fuzzification Method to Improve Small Data Set Learning Accuracy for Early Flexible Manufacturing System Scheduling
指導教授： 吳植森
Wu, Chihsen
利德江
Li, Der-Chiang
學位類別： 博士
Doctor
系所名稱： 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年： 2005
畢業學年度： 93
語文別： 英文
論文頁數： 67
中文關鍵詞： 彈性製造系統 、人工智慧、機器學習 、初期知識 、小資料學習 、資料連續化 、總合模糊 、資料領域外擴
外文關鍵詞： small data set learning, FNN, artificial intelligence, scheduling, FMS, mega-fuzzification, early knowledge, machine learning, ANN
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    • 　　過去幾年, 許多學者已針對彈性製造系統之最佳排程策略做出了很多的研究, 這當中, 許多應用人工智慧(Artificial Intelligence, AI)領域之機器學習(machine learning)的方法分別被提出來，這些方法大部分仰賴大量的學習資料以建立知識(knowledge); 但是，在彈性製造系統(Flexible Manufacturing System, FMS)初期階段所收集以建立知識的資料是少量而不完整的，這樣導致製造策略預測的準確度相對的低。然而，在競爭激烈的環境中製造決策又必須很快的做成決定，因此，提高初期知識(early knowledge)預測的準確變成一個非常具有挑戰性的問題。

    　　因此, 本論文主要著眼於小資料學習(small data set learning)方法的研究, 以提高初期彈性製造系統排程之準確度，所提出的方法包括資料連續化(data continualization)的觀念、總合模糊(mega-fuzzification)方法、模糊理論的應用、資料領域外擴(data domain external expansion)方法等。同時，本論文也考慮到資料偏斜(bias)的現象往往於小資料中發生並進而提出一個方法來修正這個現象；本論文的研究結果顯示，所提出之方法能夠有效的提高小資料學習的準確度。

    　Many machine learning methods to improve system scheduling have been proposed in the field of artificial intelligence (AI). Most of them rely on a large amount of data having been gathered, and knowledge derived from the limited data available in the early manufacturing stages is usually too fragile for a flexible manufacturing system (FMS). This causes the accuracy of prediction with regard to the production strategy to be very low. It is therefore a challenging problem to increase the accuracy of predictions derived from early knowledge acquisition. This thesis is aimed at increasing the accuracy of machine learning for FMS scheduling using small data sets. Methodologies proposed include data continualized concept, mega-fuzzification, application of fuzzy theory, and data domain external expansion approach. Also, this thesis considers the data bias phenomenon that often occurs in small data sets and provides a method for its adjustment. Furthermore, a method is proposed to determine the domain external expansion magnitude when data range is unknown. Briefly, the results of this thesis show that the proposed approaches can increase the learning accuracy in a broad range of applications.

    ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I ACKNOWLEDGEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII LIST OF FIGURES . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Research motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Proposed methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 2 Literature review for small data set learning . . . . . . . . . . . . . . . . . . 6 2.1 Machine learning research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Virtual data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 The Functional Virtual Population approach . . . . . . . . . . . . . . . . . . . . . 9 2.4 Continuous data band method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.5 Data trend estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.6 Computational Learning Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.6.1 Basic setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.6.2 True error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.6.3 PAC Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.6.4 Number of required training examples . . . . . . . . . . . . . . . . . . . . . 16 2.7 Information diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.8 The ANN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.9 The FNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Chapter 3 The structure of the FMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.1 Part processing information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 System operational assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 Input attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4 Best scheduling rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Learning and testing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Chapter 4 The proposed method and computational results . . . . . . . . . . . . . . . 33 4.1 Data continualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Mega-fuzzification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3 Learning improvement using continuous data . . . . . . . . . . . . . . . . . . . . 39 4.4 Data band external expansion for known data ranges . . . . . . . . . . . . . . 43 4.5 The number of membership functions . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.6 Type of membership functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.7 The performance of the proposed approach for known data limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Chapter 5 Determination of the domain expansion range for unknown data limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.1 Data bias adaptation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2 Determination of the expansion magnitude . . . . . . . . . . . . . . . . . . . . . . 51 5.3 The complete approach for unknown data limits . . . . . . . . . . . . . . . . . . 52 5.4 Learning results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Chapter 6 Conclusions and future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 6.2 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 BIBLIOGRAPHY 63

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