直觉模糊信息集成理论及应用
2012-1
科学出版社
Zeshui XU，Xiaoqiang Cai 著
309
《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。
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
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.
