Using Support Vector Machines, Rough Set and Optimization Algorithm for Manufacturing System
黃玉櫻、陳郁文 ; 白炳豐
E-mail: [email protected]
ABSTRACT
Diagnosing quality faults is one of the most crucial issues in manufacturing processes. Many techniques have been presented to diagnose fault in manufacturing systems. The SVM approach has received more attention due to its classification ability. However, the development of support vector machines (SVM) in the diagnosis of manufacturing systems is rare. Therefore, this thesis attempts to apply the SVM in the diagnosis of manufacturing systems. Furthermore, rough set and Immune Algorithm are employed to determine two parameters of SVM model correctly and efficiently. Five numerical examples are use to demonstrate the diagnosis ability of the proposed DSVMIA+RS (directed acyclic graph support vector machines with Immune Algorithm and rough set) model. The experiment results show that the proposed approach can classify the faulty product types correctly and efficiently.
Keywords : Multi-support vector machine ; Rough set ; Immune algorithm Table of Contents
封面內頁 簽名頁 授權書 iii 中文摘要 iv ABSTRACT v 誌謝 vi 目錄 vii 圖目錄 ix 表目錄 x 第一章 緒論 1 1.1研究背景與動機 1 1.2研究目的與方法 1 1.3研究資料 2 第二章 文獻探討 4 2.1粗略集合理論 4 2.2支援向量機 7 2.3免疫演算法 13 第三章 研究 方法與流程 17 3.1研究架構 17 3.1.1階段Ⅰ：自組織映射圖網路 19 3.1.2階段Ⅱ：粗略集合理論 20 3.1.3階段Ⅲ：DAG支援 向量機 23 3.2正確率之評估 27 第四章 研究實例 29 4.1第一組 30 4.1.1研究實例一 30 4.1.3研究實例三 34 4.2第二組 35 4.2.1 研究實例四 35 4.3第三組 36 4.3.1研究實例五 36 4.4結果分析與討論 39 第五章 結論與末來研究方向 41 5.1 結論 41 5.2未來 研究方向 42 參考文獻 43
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