臺灣博碩士論文加值系統

這篇論文提出一個嶄新的方法是以模糊積分為基礎來整合多個補償性類神經模糊網路。以每個分類器的互補性來整合整體系統的架構達到克服單個分類器的限制。模糊積分會比多數投票的方法更好的架構是由於利用定義分類器之間的關連作整合的方法。然而為了使資料的分類率比傳統的方法達到更高準確度，一個以模糊積分整合多個補償性類神經模糊網路的想法就產生了。我們首先提出模糊積分用於多個補償性類神經模糊網路的整合，也利用補償性類神經模糊網路中補償程度因子的特性成功建構出整體的系統架構。我們所提出方法最重要的優點不只是考慮到結合每個分類器的輸出值，也將不同的網路間相互的重要關連作了考量。實驗模擬的結果顯示以模糊積分整合多個補償性類神經模糊網路的表現比傳統的多數投票方法還要更好。

This thesis presents a novel method for combining multiple compensatory neural fuzzy networks (CNFNs) using fuzzy integral. The fusion of multiple classifiers can overcome the limitations of a single classifier since the classifiers complemen each other. A fuzzy integral is a better combination scheme than majority voting method that uses the subjectively defined relevance of classifiers. A combination of multiple CNFN classifiers with fuzzy integral (FI) is proposed to achieve data classification with higher accurate than existing traditional methods. We first apply multiple CNFNs fusion using fuzzy integral based on compensatory degree to establish the classification model. The advantage of the proposed method is that not only are the classification results combined but the relative importance of the different networks is also considered. Experimental results show that the fusion of multiple CNFNs using fuzzy integral can perform better than existing traditional methods.

CHAPTER 1 INTRODUCTION 1
CHAPTER 2 THE COMPENSATORY NEURAL FUZZY NETWORK 7
2.1 STRUCTURE OF THE CNFN MODEL 8
2.2 AN ON-LINE LEARNING ALGORITHM 12
2.2.1 Structure Learning Scheme 13
2.2.2 Parameter Learning Scheme 15
CHAPTER 3 FUZZY INTEGRAL 19
3.1 SUGENO FUZZY MEASURE 19
3.2 SUGENO FUZZY INTEGRAL 21
3.3 CHOQUET INTEGRAL 23
CHAPTER 4 MULTIPLE CNFNS FUSION USING FUZZY INTEGRAL 27
4.1 DATA PREPROCESSING 27
4.2 THE ARCHITECHTURE OF MULTIPLE CNFNS FUSION 29
CHAPTER 5 SIMULATION RESULTS 31
5.1 EXPERMENTAL RESULTS ON IRIS DATA 32
5.2 EXPERIMENTAL RESULTS ON WISCONSIN BREAST CANCER DIAGNOSTIC DATA 35
CHAPTER 6 ANALYSIS AND DISCUSSION 38
6.1 COMPARED PERFORMANCE WITH FUZZY METHOD AND NON-FUZZY METHOD 39
6.1.1 Analysis Classification Accuracy on Iris Data Set 39
6.1.2 Analysis Classification Accuracy on the Wisconsin Breast Cancer Diagnostic Data Set 41
6.2 ANALYSIS THE NUMBER OF CNFN CLASSIFIERS 43
6.2.1 Analysis the Number of CNFNs on Iris Data Set 44
6.2.2 Analysis the Number of CNFNs on the Wisconsin Breast Cancer Diagnostic Data Set 44
6.3 DISCUSSION 45
CHAPTER 7 CONCLUSION AND FUTURE WORKS 47
7.1 CONCLUSION 47
7.2 FUTURE WORKS 49
BIBLIOGRAPHY 50
個人簡歷 55
PUBLISH LIST 56

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