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研究生:王智永
研究生(外文):Wang, ZhiYong
論文名稱:新模糊數相似性方法與模糊數聚類演算法之設計
論文名稱(外文):Design New Fuzzy-Number Similarity Measures and Fuzzy-Number Clustering Method
指導教授:陳士杰陳士杰引用關係
指導教授(外文):Chen, Shijie
口試委員:陳振東鄭妃君
口試委員(外文):Chen, ZhendongCheng, Feijun
口試日期:2012-06-27
學位類別:碩士
校院名稱:國立聯合大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:72
中文關鍵詞:一般化模糊數區間值模糊數標準差映射距離相似度測量
外文關鍵詞:Generalized fuzzy numberMap distanceSimilarity measureInterval-Valued Fuzzy NumbersClustering analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:136
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  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
本研究提出新的模糊數相似度量測(Fuzzy-number Similarity Measure)方法,透過映射距離(Map distance)和標準差(Standard deviation)運算的結合,來衡量一般化模糊數(Generalized fuzzy numbers)以及區間值模糊數(Interval-valued fuzzy numbers)之間的相似程度。同時也對此方法做了一些相關的性質證明,並利用50組一般化模糊數以及21組區間值模糊數所提出的模糊數相似度測量方法和過去的模糊數相似度量測法做比較,其結果顯示了本研究所提出的一般化模糊數及區間值模糊數方法皆優於現有的一般化模糊數及區間值模糊數相似度量測方法。此外,我們會以此方法加上考量評估準則以及評估權重,提出一個擴展之新方法,用以處理模糊聚類問題。首先我們決定一個語意值,接著計算其相似程度,依據計算結果建構出階層分群樹並區分成不同的群體,我們將利用一個範例說明這個新的模糊數聚類演算法可以在實際的應用上更有效的處理問題。
This study proposes new methods based on map distance to measure the degree of similarity between generalized and interval-valued fuzzy numbers. Some properties of the proposed similarity measures are demonstrated here. There are 50 sets of generalized fuzzy numbers and 21 sets of interval-valued fuzzy numbers are adopted to compare the proposed methods with some existing similarity measures for proving the proposed similarity measures are better than the existing methods.
Furthermore, the proposed similarity measure is used to deal with fuzzy-number cluster problems. We present a new method for handling the fuzzy clustering problems of which the characteristic values and weights of indices are generalized fuzzy numbers. The proposed mechanism is based on the fuzzy-number similarity measure. Firstly determine the linguistic evaluating values and the linguistic weights of each evaluating criterion with respect to the alternatives. Then measure the degree of similarity between two arbitrary weighted evaluating values on the same criterion. Finally constructing the hierarchical cluster tree and generated different clusters. A numerical example is demonstrated the new mechanism.

CHAPTER 1 Introduction
1.1 Motivation
1.2 Organization of this thesis

CHAPTER 2 Literatures Review
2.1 Standard deviation
2.2 Generalized and interval-valued fuzzy numbers
2.3 Interval-Valued fuzzy numbers
2.4 The existing similarity measures between generalized fuzzy numbers
2.5 The existing similarity measures between interval-valued fuzzy numbers

CHAPTER 3 New Method to Calculate the Degree of Similarity between Generalized
Fuzzy Numbers
3.1 Analysis of the existing similarity measure
3.2 New similarity measure between generalized fuzzy numbers based on standard deviation and map distance
3.3 Comparing existing similarity measure with the proposed similarity measure

CHAPTER 4 New Method to Calculate the Degree of Similarity between Interval-Valued Fuzzy Numbers
4.1 New similarity measure between interval-valued trapezoidal fuzzy numbers based on standard deviation and map distance
4.2 Comparing existing methods with the proposed similarity measure

CHAPTER 5 A Hierarchical Clustering Method Based on Fuzzy- Number Similarity Measure Applied to a Problem of Grouping Profiles
5.1 New Fuzzy-number hierarchical clustering method
5.2 Illuatrative example

CHAPTER 6 Conclusions

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