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研究生:李易儒
研究生(外文):Yi-Ru Li
論文名稱:應用區間數值的物理意義於分群及預測模型建構
論文名稱(外文):Symbolic Data Processing with Physical Meanings of Interval Values for Clustering and Forecasting
指導教授:蘇順豐
指導教授(外文):Shun-Feng Su
口試委員:蘇順豐
口試委員(外文):Shun-Feng Su
口試日期:2015-07-20
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:74
中文關鍵詞:區間型態資料模糊c 均值分群演算法徑向 基底函數網路區間數值的物理意義
外文關鍵詞:Symbolic interval-valued dataFuzzy c-means (FCM)Radial basis function networks (RBFN)Physical meanings of interval values
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本論文針對區間型態的資料的分群及預測進行分析及探討。區間型態資料和單點型態資料,從表面數字來說僅有資料個數的差別而已(單個數值及兩個數值),然而,區間型態資料並非只有表面數字上的意涵,其區間數值也具有數字代表的物理意義。因此本篇論文提出一種考量區間型態資料物理意義的改良式模糊c均值演算法(Fuzzy c-means, FCM),將range的值視為區間資料中心點的可靠程度參考,於目標函式乘上一個與range成反比的exponential函數,如此便能降低range較大(較不可靠)的資料對分群結果的影響,在後續的實驗結果中也能看到同時考慮數值及物理意義的改良式分群法對於分群結果的改善。另一方面,分群後各群的質心點能作為其預測模型的區域特徵點,且區間型態資料的物理意義也能於徑向基底函數網路預測模型中運用,以提升預測模型的效能。
In this thesis, interval-valued data clustering methods and forecasting models are studied and discussed. From a very simple viewpoint, the difference between interval-valued data and single-point data is just the number of data attributes for the dataset. However, those attributes (lower/upper bounds or centre/range) have physical meanings. In this study, an adaptive fuzzy c-means clustering method is proposed to include physical meanings of interval values. The range is regarded as the confidence of the centre data and an exponential function which is inverse proportion to the range is added into the objective function to account for this confidence idea. Then the data with less confidence obtain less influence in the training process. It can be found from our experiments that with both numerical and physical meanings in our approach, the clustering performance is improved and this improvement can also benefit the following data analysis process. With the cluster centroids obtained from the proposed adaptive fuzzy c-means clustering method in radial basis function networks (RBFN), the forecasting model can be constructed. With the same idea of including physical meaning of interval value in the training process, the obtained model can have better predictions as shown in our experiments.
Content
摘要 I
Abstract II
誌謝 III
Content IV
Figure List V
Table List VI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation and Objective 3
1.3 Organization 3
Chapter 2 The Traditional Clustering Algorithm and Proposed Fuzzy C-Means Clustering Method 5
2.1 Fuzzy C-Means Clustering Algorithm 5
2.2 Interval Fuzzy C-Means Clustering Algorithm 10
2.2.1 The Lower and Upper Bounds in Interval Fuzzy C-Means 11
2.2.2 The Centre and Range in Interval Fuzzy C-Means 13
2.3 Adaptive Fuzzy C-Means Clustering Method 14
Chapter 3 The Traditional forecasting Model and Proposed Interval Radial Basis Function Network 19
3.1 Radial Basis Function Networks 19
3.2 Interval Radial Basis Function Networks 27
3.3 Adaptive Interval Radial Basis Function Network 28
Chapter 4 Experimental Results 32
4.1 Adaptive Fuzzy C-Means Clustering Algorithm 32
4.1.1 Synthetic Symbolic Interval-Valued Data Experiment 33
4.1.2 City Temperature Symbolic Interval Data Experiment 37
4.1.3 Car Symbolic Interval Data Experiment 40
4.1.4 Time Consumption 43
4.2 Adaptive Radial Basis Function Network 44
4.2.1 Time Series Interval Data Forecasting Pre-Processing 45
4.2.2 Synthetic Mackey-Glass Interval Data Experiment 46
4.2.3 The USD/TWD Exchange Rate Data Experiment 53
Chapter 5 Conclusions and Future Work 60
5.1 Conclusions 60
5.2 Future Work 61
References 62
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