Self-contained introduction to the field of hesitant fuzzy sets
Presents the state-of-the-art in hesitant fuzzy theory and applications
Written by a leading expert in the field
This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) characterized by uncertain ("hesitant") information. Because of its clarity and self contained explanations, the book can also be adopted as a textbook from graduate and advanced undergraduate students.
Hesitant Fuzzy Aggregation Operators and Their Applications.- Distance, Similarity, Correlation, Entropy Measures and Clustering.- Algorithms for Hesitant Fuzzy Information.- Hesitant Preference Relations.- Hesitant Fuzzy MADM Models.
This book presents a systematic introduction to hesitant fuzzy theory. Coverage includes hesitant fuzzy aggregation techniques, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods.
Inhaltsverzeichnis
Hesitant Fuzzy Aggregation Operators and Their Applications.- Distance, Similarity, Correlation, Entropy Measures and Clustering.- Algorithms for Hesitant Fuzzy Information.- Hesitant Preference Relations.- Hesitant Fuzzy MADM Models.
Klappentext
This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) characterized by uncertain ("hesitant") information. Because of its clarity and self contained explanations, the book can also be adopted as a textbook from graduate and advanced undergraduate students.
Self-contained introduction to the field of hesitant fuzzy sets
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Presents the state-of-the-art in hesitant fuzzy theory and applications
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Written by a leading expert in the field
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