臺灣博碩士論文加值系統

在稀疏模糊規則庫系統中，模糊規則庫通常不完整，在這種情形下，系統可能無法完整執行模糊推理以取得合理的推論結果。為了克服此缺點，我們需要發展在稀疏模糊規則系統中作模糊內插推理之技術。在本論文中，我們提出一個新的模糊內插推理方法，主要使用α切割(α-cut)與轉換的技術，其可以產生比目前已存在的方法更合理的推論結果。另外，我們也延伸了α切割與轉換的技術提出一個新方法以處理在稀疏模糊規則系統中作加權模糊內插推理之新方法，對於模糊規則具多前提變數之模糊內插推理，其允許模糊規則的前置部份考慮權重因素。本論文所提的方法可以很有用的在模糊規則庫系統中作模糊內插推理。

In sparse fuzzy rule-based systems, the fuzzy rule bases usually are incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpola¬tive reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. Moreover, we also extend the α-cuts and transformation techniques to present a new method to handle the weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. For multiple antecedent variables fuzzy rules interpolation, the proposed method allows each linguistic variable appearing in the antecedent parts of fuzzy rules associated with a weighting factor. The proposed methods provide a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

Abstract in Chinese i
Abstract in English ii
Acknowledgements iii
Contents iv
List of Figures and Tables vi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Literature 1
1.3 Organization of This Thesis 4
Chapter 2 Fuzzy Set Theory and Fuzzy Interpolative Reasoning Method for Sparse fuzzy Rule 5
2.1 Basic Concepts of Fuzzy Sets 5
2.2 Basic Concepts of Fuzzy Interpolative Reasoning 10
Chapter 3 A Review of The Existing Fuzzy Interpolative Reasoning Methods 12
3.1 The KH Method 12
3.2 The MACI Method 14
3.3 The IMUL Method for Multidimensional Input Space 14
3.4 The HS Method 17
Chapter 4 A New Fuzzy Interpolative Reasoning Method for Sparse Fuzzy Rule-Based Systems 20
4.1 Single Antecedent Variable with Trapezoidal Fuzzy Sets 20
4.2 Single Antecedent Variable with Triangular Fuzzy Sets 27
4.3 Single Antecedent Variable with Hexagonal Fuzzy Sets 33
4.4 Single Antecedent Variable with Polygonal fuzzy sets 41
4.5 Single Antecedent Variable with Gaussian Fuzzy Sets 48
4.6 General Multiple Antecedent Variables Interpolation 50
4.7 Experimental Results 57
4.8 Summary 66
Chapter 5 Weighted Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems 67
5.1 Multiple Antecedent Variables with Polygonal Fuzzy Sets 67
5.2 Multiple Antecedent Variables with Bell-Shaped Fuzzy Sets 75
5.3 A Method to Automatically Tune the Optimal Weights of the Antecedent Variables Appearing in the Antecedent Parts of Fuzzy Rules 81
5.4 Summary 88
Chapter 6 Conclusions 89
6.1 Contributions of This Thesis 89
6.2 Future Research 90
References 91

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