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Author: Stenly Ibrahim Adam
Stenly Ibrahim Adam
Thesis Title: 在稀疏模糊規則庫系統中作加權式模糊內插推論及自適性模糊內插推論之新方法
New Methods for Weighted Fuzzy Interpolated Reasoning and Adaptive Fuzzy Interpolation for Sparse Fuzzy Rule-Based Systems
Advisor: 陳錫明
Shyi-Ming Chen
Committee: 陳錫明
Shyi-Ming, Chen
程守雄
Shou-Hsing, Cheng
呂永和
Yung-Ho, Leu
李惠明
Huey-Ming, Lee
蕭瑛東
Ying-Dong, Hsiao
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2017
Graduation Academic Year: 105
Language: 英文
Pages: 122
Keywords (in Chinese): Fuzzy Interpolative ReasoningSparse Fuzzy Rule-Based SystemsRanking ValuesPolygonal Fuzzy SetsScale and Move Transformation TechniquesAdaptive Fuzzy Interpolative ReasoningGeneral Representative ValuesShift and Modification Techniques
Keywords (in other languages): Fuzzy Interpolative Reasoning, Sparse Fuzzy Rule-Based Systems, Ranking Values, Polygonal Fuzzy Sets, Scale and Move Transformation Techniques, Adaptive Fuzzy Interpolative Reasoning, General Representative Values, Shift and Modification Techniques
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  • Fuzzy interpolative reasoning is a very important research topic in sparse fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems. In the first method, we propose a new transformation-based weighted fuzzy interpolative reasoning method based on the ranking values of polygonal fuzzy sets and the proposed scale and move transformation techniques. The proposed weighted fuzzy interpolative reasoning method is based on the multiple fuzzy rules and multiple antecedent variables fuzzy interpolative reasoning scheme, which can automatically calculate the weight of each fuzzy rule and can automatically calculate the weight of each antecedent variable of the fuzzy rules. The proposed scale and move transformation techniques can deal with singleton fuzzy sets and polygonal fuzzy sets. In the second method, we propose a new adaptive fuzzy interpolative reasoning method based on general representative values of polygonal fuzzy sets and the proposed shift and modification techniques. The proposed adaptive fuzzy interpolative reasoning method includes a new contradiction solving method to get a higher similarity degree between polygonal fuzzy sets of the adaptive fuzzy interpolative reasoning results. The experimental results show that the proposed weighted fuzzy interpolative reasoning method and the proposed adaptive fuzzy interpolation for sparse fuzzy rule-based systems outperforms the existing methods


    Fuzzy interpolative reasoning is a very important research topic in sparse fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems. In the first method, we propose a new transformation-based weighted fuzzy interpolative reasoning method based on the ranking values of polygonal fuzzy sets and the proposed scale and move transformation techniques. The proposed weighted fuzzy interpolative reasoning method is based on the multiple fuzzy rules and multiple antecedent variables fuzzy interpolative reasoning scheme, which can automatically calculate the weight of each fuzzy rule and can automatically calculate the weight of each antecedent variable of the fuzzy rules. The proposed scale and move transformation techniques can deal with singleton fuzzy sets and polygonal fuzzy sets. In the second method, we propose a new adaptive fuzzy interpolative reasoning method based on general representative values of polygonal fuzzy sets and the proposed shift and modification techniques. The proposed adaptive fuzzy interpolative reasoning method includes a new contradiction solving method to get a higher similarity degree between polygonal fuzzy sets of the adaptive fuzzy interpolative reasoning results. The experimental results show that the proposed weighted fuzzy interpolative reasoning method and the proposed adaptive fuzzy interpolation for sparse fuzzy rule-based systems outperforms the existing methods

    Abstract i Acknowledgements ii Contents iii List of Figures and Tables v Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Related Literature 6 1.3 Organization of This Thesis 11 Chapter 2 Preliminaries 12 2.1 Basic Concepts of Fuzzy Sets 12 2.2 Characteristic Points of Polygonal Fuzzy Sets 12 2.3 Summary 13 Chapter 3 Weighted Fuzzy Interpolated Reasoning Based on Ranking Values of Polygonal Fuzzy Sets and Scale and Move Transformation Techniques 15 3.1 Ranking Values of Polygonal Fuzzy Sets 15 3.2 A New Weighted Fuzzy Interpolated Reasoning Based on Ranking Values of Polygonal Fuzzy Sets and Scale and Move Transformation Techniques 16 3.3 A Comparison of Fuzzy Interpolative Reasoning Results for the Proposed Method and the Existing Methods 28 3.4 Summary 81 Chapter 4 Adaptive Fuzzy Interpolation Based on General Representative Values of Polygonal Fuzzy Sets and the Shift and Modification Techniques 82 4.1 Preliminaries 82 4.2 A New Adaptive Fuzzy Interpolation Based on General Representative Values of Polygonal Fuzzy Sets and the Shift and Modification Techniques 83 4.3 A Comparison of the Proposed Adaptive Fuzzy Interpolation Method and the Existing Methods 95 4.4 Summary 100 Chapter 5 Conclusions 102 5.1 Contributions of This Thesis 102 5.2 Future Research 102 References 104

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