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Author: Dipto Barman
Dipto Barman
Thesis Title: 根據多邊形模糊集合以作自適性模糊內插推論 及根據區間Type-2模糊集合以作自適性加權式模糊內插推論之新方法
Adaptive Fuzzy Interpolative Reasoning Based on Polygonal Fuzzy Sets and Adaptive Weighted Fuzzy Interpolative Reasoning Based on Interval Type-2 Fuzzy Sets
Advisor: 陳錫明
Shyi-Ming Chen
Committee: 陳錫明
程守雄
壽大衛
呂永和
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2019
Graduation Academic Year: 107
Language: 英文
Pages: 154
Keywords (in Chinese): Adaptive Fuzzy Interpolative ReasoningFuzzy RulesSparse Fuzzy Rule-Based SystemsInterval Type-2 Fuzzy SetsRepresentative ValuesPolygonal Fuzzy SetsRanking ValuesContradiction Measures
Keywords (in other languages): Adaptive Fuzzy Interpolative Reasoning, Fuzzy Rules, Sparse Fuzzy Rule-Based Systems, Interval Type-2 Fuzzy Sets, Representative Values, Polygonal Fuzzy Sets, Ranking Values, Contradiction Measures
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Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. In this thesis, we propose two new adaptive fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on polygonal fuzzy sets and interval type-2 fuzzy sets, respectively. In the first method of our thesis, we propose a new adaptive fuzzy interpolative reasoning method based on contradiction measures between polygonal fuzzy sets and novel move and transformation techniques. The proposed adaptive fuzzy interpolative reasoning method performs fuzzy interpolative reasoning using the multiple fuzzy rules with multiple antecedent variables fuzzy interpolative reasoning scheme and solves the contradictions after the fuzzy interpolative reasoning processes based on contradiction measures between polygonal fuzzy sets. The experimental results show that the proposed adaptive fuzzy interpolative reasoning method outperforms the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. In the second method of our thesis, we propose a new adaptive weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on representative values and similarity measures of interval type-2 fuzzy sets. The experimental results show that the proposed adaptive weighted fuzzy interpolative reasoning method can overcome the drawbacks of the existing adaptive fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems.


Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. In this thesis, we propose two new adaptive fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on polygonal fuzzy sets and interval type-2 fuzzy sets, respectively. In the first method of our thesis, we propose a new adaptive fuzzy interpolative reasoning method based on contradiction measures between polygonal fuzzy sets and novel move and transformation techniques. The proposed adaptive fuzzy interpolative reasoning method performs fuzzy interpolative reasoning using the multiple fuzzy rules with multiple antecedent variables fuzzy interpolative reasoning scheme and solves the contradictions after the fuzzy interpolative reasoning processes based on contradiction measures between polygonal fuzzy sets. The experimental results show that the proposed adaptive fuzzy interpolative reasoning method outperforms the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. In the second method of our thesis, we propose a new adaptive weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on representative values and similarity measures of interval type-2 fuzzy sets. The experimental results show that the proposed adaptive weighted fuzzy interpolative reasoning method can overcome the drawbacks of the existing adaptive fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems.

ABSTRACT iii Acknowledgements iv CONTENTS v List of Figures and Tables vii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Related Literature 4 1.3 Organization of This Thesis 8 Chapter 2 Preliminaries 9 2.1 Basic Concepts of Fuzzy Sets 9 2.2 Characteristic Points of Interval type-2 Polygonal Fuzzy Sets 10 2.3 Summary 11 Chapter 3 Adaptive Fuzzy Interpolative reasoning Based on Contradiction Measures of Polygonal Fuzzy Sets and Novel Move and Transformation Techniques 12 3.1 Preliminaries 12 3.2 A New Adaptive Fuzzy Interpolative Reasoning Based on Contradiction Measures of Polygonal Fuzzy Sets and Novel Move and Transformation Techniques 14 3.3 A Comparison of Adaptive Fuzzy Interpolative Reasoning Results between the Proposed Method and the Existing Methods 25 3.4 Summary 32 Chapter 4 Adaptive Weighted Fuzzy Interpolative Reasoning Based on Representative Values and Similarity Measures of Interval Type-2 Fuzzy Sets 33 4.1 Preliminaries 33 4.2 A New Adaptive Weighted Fuzzy Interpolative Reasoning Based on Representative Values and Similarity Measures of Interval Type-2 Polygonal Fuzzy Sets 35 4.3 A Comparison of Adaptive Weighted Fuzzy Interpolative Reasoning Results between the Proposed Method and the Existing Methods 49 4.4 Summary 142 Chapter 5 Conclusions 143 5.1 Contributions of This Thesis 143 5.2 Future Research 144 References 145

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