直觉模糊信息集成理论及应用

Chapter 1 Intuitionistic Fuzzy Information Aggregation

1.1Intuitionistic Fuzzy Sets

1.2 Operational Laws of Intuitionistic Fuzzy Numbers

1.3 Intuitionistic Fuzzy Aggregation Operators

1.4 Intuitionistic Fuzzy Bonferroni Means

1.5 Generalized Intuitionistic Fuzzy Aggregation Operators

1.6 Intrutionistic Fuzzy Aggregation Operators Based on Choquet Integral

1.7Induced Generalized Intuitionistic Fuzzy Aggregation Operators

References

Chapter 2 Interval-Valued Intuitionistic Fuzzy Information Aggregation

2.1 Interval-Valued Intuitionistic Fuzzy Sets

2.2 Operational Laws of lnterval-Valued Intuitionistic Fuzzy Numbers

2.3 Interval-Valued Intuitionistic Fuzzy Aggregation Operators

2.4 Interval-Valued Intuitiorustic Fuzzy Bonferroni Means

2.5 Generalized Interval-Valued Intuitionistic Fuzzy Aggregation Operators

2.6 Interval-Valued Intuitionistic Fuzzy Aggregation Operators Based on Choquet Integral

2.7 Induced Generalized Interval-Valued Intuitionistic Fuzzy Aggregation Operators

References

Chapter 3 Correlation Distance and Similarity Measures of Intuitionistic Fuzzy Sets

3.1 Correlation Measures of Intuitionistic Fuzzy Sets

3.2 Distance and Similarity Measures of Intuitionistic Fuzzy Sets

3.3 Distance and Similarity Measures of Interval-Valued Intuitionistic Fuzzy Sets

3.3.1 Distance and Similarity Measures Based on Geometric Distance Models

3.3.2 Distance and Similarity Measures Based on Set-Theoretic Approaches

References

Chapter 4 Decision Making Models and Approaches Based on Intuitionistic Preference Relations

4.1 Intuitionistic Preference Relations

4.2 Group Decision Making Based on Intuitionistic Preference Relations

4.3 Incomplete Intuitionistic Preference Relations

4.4 Group Decision Making Based on Incomplete Intuitionistic Preference Relations

4.5 Interval-Valued Intuitionistic Preference Relations

4.6 Group Decision Making Based on Interval-Valued Intuitionistic Preference Relations .

4.7 Group Decision Making Based on Incomplete Interval-Valued Intuitionistic Preference Relations

4.8 Multi-Attribute Decision Making with Intuitionistic Fuzzy Preference Information on Alternatives

4.8.1 Consistent Intuitionistic Preference Relations

4.8.2 Linear Programming Models with Intuitionistic Fuzzy Information

4.8.3 Intuitionistic Fuzzy Decision Making Based on Linear Programming Models

4.9 Multi-Attribute Decision Making Based on Various Intuitionistic Preference Structures

4.9.1 Multi-Attribute Decision Making Models Based on Intuitionistic Preference Relations

4.9.2 Multi-Attribute Decision Making Models Based on Incomplete Intuitionistic Preference Relations

4.9.3 Multi-Attribute Decision Making Models Based on Different Types of Intuitionistic Preference Relations

4.10 Consistency Analysis on Group Decision Making with Intuitionistic Preference Relations

4.11 Consistency Analysis on Group Decision Making with Interval-Valued Intuitionistic Preference Relations

References

Chapter 5 Projection Model-Based Approaches to Intuitionistic Fuzzy Multi-Attribute Decision Making

5.1 Multi-Attribute Decision Making with Intuitionistic Fuzzy Information

5.2 Multi-Attribute Decision Making with Interval-Valued Intuitionistic Fuzzy Information

References

Chapter 6 Dynamic Intuitionistic Fuzzy Multi-Attribute Decision Making

6.1 Dynamic Intuitionistic Fuzzy Weighted Averaging Operators

6.2 Dynamic Intuitionistic Fuzzy Multi-Attribute Decision Making

6.3 Uncertain Dynamic Intuitionistic Fuzzy Multi-Attribute Decision Making

References

Chapter 7 Nonlinear Optimization Models for Multi-Attribute Group Decision Making with Intuitionistic Fuzzy Information

7.1 Nonlinear Optimization Models for Determining Decision Makers' Weights

7.2 Extended Nonlinear Optimization Models in Interval-Valued Intuitionistic Fuzzy Situations

7.3 Numerical Analysis

References

Index

《直觉模糊信息集成理论及应用（英文版）》将主要对近年来国内外学者特别是作者本人在直觉模糊信息的集成和处理方式，以及相关的直觉模糊决策模型和方法等最新研究成果进行深入系统的介绍。《直觉模糊信息集成理论及应用（英文版）》共分为七章。可作为模糊数学、运筹学、信息科学和管理科学与工程等领域的研究人员和工程技术人员的参考书，以及高等院校有关专业高年级本科生和研究牛的教学用书。

Intuitionistic Fuzzy Information Aggregation： Theory and Applications， the first book to provide a thorough and systematic introduction to the intuitionistic fuzzyaggregation methods， the correlation， distance and similarity measures of in tuitionisticfuzzy sets， and various decision-making models and approaches based on the above-mentioned information processing tools. Through numerous practical examples andillustrations with tables and figures， it offers researchers and professionals in the field of fuzzy mathematics， information fusion and decision analysis， the most recentresearch findings developed by the authors.