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研究生: 邱聖家
論文名稱: 使用相似性指標辨識萃智解答模型:以相關趨勢辨識為例
Using Similarity Measures to Identify TRIZ Model of Solutions: Examples of Relevant Trends Identification
指導教授: 許棟樑
口試委員: 夏太長
黃乾怡
學位類別: 碩士
Master
系所名稱: 工學院 - 工業工程與工程管理學系
Department of Industrial Engineering and Engineering Management
論文出版年: 2013
畢業學年度: 101
語文別: 中文
論文頁數: 188
中文關鍵詞: 功能屬性分析技術演化趨勢相似性比對萃智電腦輔助創新解題
外文關鍵詞: Computer-aided Problem Solving, Function and Attribute Analysis,, Technical Trends, Trends of Engineering Systems Evolution
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  • 基於「類似問題有類似解答」之概念,問題若具有相似的特性,通常亦含有相似的解答特徵。因此,本研究將過去解題案例建構為案例資料庫,問題的求解即可依據問題特徵陣列之相似性比對,判斷與問題最相似的過去解題案例並將此案例解答模型作為問題的參考解答。
    本研究使用相似性指標發展演化趨勢辨識系統,而於演化趨勢辨識過程中,首先為問題與趨勢階段屬性特徵之比對,如兩者比對相符則可再自此趨勢階段跳躍至具有問題所需功能的後續階段,並且該趨勢階段就能作為問題的觸發解。經由演化趨勢辨識軟體的建立,即可自動、快速地辨識問題相關趨勢,而不需再仰賴專家經驗或知識。最後本研究以124個案例與51個演化趨勢進行K-fold cross-validation驗證,測試此數理方法的有效性,而其結果證明本研究的方法所推薦前10名趨勢觸發解可100%命中案例原本解答,遠超過隨機挑選10個趨勢觸發解的9%案例解答命中率。
    本研究的貢獻包含:1)以數理方式發展客觀且快速辨識解答模型之方法,開拓一個萃智科學研究之方向; 2)配合電腦化工具,建立一個電腦輔助的解題工具,經由過去案例把專家經驗引入系統中,提供解題者一個快速、客觀且穩定之電腦化系統。


    Based on the concept of “Like Problem, like solutions” in TRIZ theory, the problems with similar problem characteristics are likely to have similar solution characteristics. With a set of known solved problems and their corresponding solutions as a casebase, solving a problem becomes a matter of identifying highly similar known problems in terms of Problem Characteristic Array similarity and integrating the Solution Arrays of the corresponding similar problems to form the set of solution models.

    In this research, relevant trends identification system using similarity measures were developed. In identifying relevant trend solutions, characteristic attributes of the problem are compared against the characteristic attributes of certain earlier stage of a trend first. If they match, the ensuing stages of the same trend can imply model of solutions as jumping into that stage can provide functions needed to solve the problem. By encoding the 'knowledge' embedded in the trends, a piece of software is written to identify the relevant trends for problem solving quickly and objectively without needing to rely on expert experience and knowledge. K-fold validity verification was used to verify the effectiveness of this method. With 124 known cases and 51 trend examples, the results showed that the solutions recommended by the 10 most likely trends achieved 100% coverage of problem known solutions and is significantly better than randomly selected 10 solutions which covered less than 9% of the known solutions.

    The contributions of this research include: 1) Opening up a new branch of TRIZ research using mathematical methods to objectively identify model of solutions. 2) Establishing a computer aided problem-solving tool that can automatically and quickly identify the relevant trends for problem solving.

    第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 1 1.3研究架構 2 第二章 文獻探討 4 2.1萃智簡介 4 2.2演化趨勢應用 7 2.3數理化方法 14 2.3.1 相似性比對概念 14 2.3.2 分類方法 16 2.4田口方法 24 2.4.1直交表 24 2.4.2 信號雜音比(SN比) 26 第三章 研究方法 29 3.1 TRIZ一般解題步驟 29 3.2標準化陣列 30 3.2.1問題特徵陣列(Problem Characteristic Array, PCA) 31 3.2.2解答陣列(Solution Array, SA) 32 3.2.3案例陣列(Case Array, CA) 33 3.2.4趨勢特徵陣列(Trend Characteristic Array, TCA) 33 3.3數理化解題工具 34 3.3.1問題解答相似性比對概念 34 3.4演化趨勢相似性應用 35 3.4.1演化趨勢之比對 35 3.4.2演化趨勢相似性之計算 36 3.4.3演化趨勢數理化解題模式 41 3.4.4應用田口方法於相似性計算 57 3.4.5工程演化趨勢問卷設計 59 3.5案例資料庫驗證-K疊交叉驗證法(K-FOLD CROSS-VALIDATION) 60 第四章 軟體建構 64 4.1問題的輸入 67 4.2解答模型輸出 68 第五章 案例驗證 76 5.1田口式實驗設計 76 5.1.1定義問題 77 5.1.2決定控制因子與其水準 78 5.1.3測試控制因子水準是否滿足非(0-0)情況 79 5.1.4決定最佳參數組合 80 5.2 K-FOLD CROSS-VALIDATION驗證 84 5.3工程演化趨勢問卷驗證 93 5.4驗證成果 99 第六章 結論與建議 101 6.1結論 101 6.2建議與未來研究方向 102 參考文獻 103 附錄一 107 附錄二 121

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