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題名: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
作者:張峯銘
作者(外文):Fengming Chang
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系碩博士班
指導教授:吳植森
利德江
學位類別:博士
出版日期:2005
主題關鍵詞: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small data set learningFNNartificial intelligenceschedulingFMSmega-fuzzificationearly knowledgemachine learningANN
原始連結:連回原系統網址new window
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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.
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