Techniques of Decision Making, Uncertain Reasoning and Regression Analysis Under the Hesitant Fuzzy Environment and Their Applications

This book mainly introduces some techniques of decision-making, uncertain reasoning and regression analysis under the hesitant fuzzy environment and expands the applications of hesitant fuzzy sets in solving practical problems. The book pursues three major objectives: (1) to introduce some techniques about decision-making, uncertain reasoning and regression analysis under the hesitant fuzzy environment, (2) to prove these techniques theoretically and (3) to apply the involved techniques to practical problems. The book is especially valuable for readers to understand how hesitant fuzzy set could be employed in decision-making, uncertain reasoning and regression analysis and motivates researchers to expand more application fields of hesitant fuzzy set.

Provides theories and techniques to model several types of hesitant fuzzy information Introduces techniques to solve group decision-making, uncertain reasoning, and regression analysis problems Identifies the approaches by practical applications, such as the route selection of arctic northwest passage

Autorentext

Chenyang Song received the Bachelor Degree in meteorology from PLA University of Science and Technology, Nanjing, China, in 2014, the Master Degree in meteorology from PLA University of Science and Technology, Nanjing, China, in 2017, and the Ph.D degree in computer science from the Army Engineering University of PLA, Nanjing, China, in 2020. He is currently an engineer at the Army Aviation Institute, Beijing, China. He has published more than ten peer reviewed papers, and his research works has been published in Applied Soft Computing, International Journal of Intelligent Systems, International Journal of Fuzzy Systems, Applied Intelligence, International Journal of Machine Learning and Cybernetics, Journal of Intelligent and Fuzzy Systems, etc. His current research interests include decision analysis, optimization and data fusion. Zeshui Xu received the Ph. D degree in management science and engineering from Southeast University, Nanjing, China, in 2003.From October 2005 to December 2007, he was a Postdoctoral Researcher with School of Economics and Management, Tsinghua University, China. He was a Distinguished Young Scholar of the National Natural Science Foundation of China and the Chang Jiang Scholar of the Ministry of Education of China. He is currently a Professor with the Business School, Sichuan University, Chengdu. He has been elected as Academician of IASCYS, Fellow of IEEE, IFSA, RSA, IET, ORS, BCS, VEBLEO, IAAM, AAIA, and ranked 30th in 2019 single year scientific impact and 258th in career scientific impact among World's top 100,000 Scientists, and ranked 57th among World's top 1,000 Scientists in Computer Science & Electronics in 2020. He has published 17 monographs by Springer and contributed more than 650 SCI/SSCI articles to professional journals. He is among the world's top 1% most highly cited researchers with about 67,000 citations in Google Scholar, his h-index is 133. He is currently the Associate Editors of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, IEEE Access, Information Sciences, Artificial Intelligence Review, Cognitive Computation, Applied Intelligence, Journal of the Operational Research, Fuzzy Optimization and Decision Making, etc. His current research interests include Decision-making theory and methodology, optimization algorithms, information fusion, and big data analytics.

Inhalt
Chapter 1 This chapter introduces the background about decision making, uncertain reasoning and regression analysis under the hesitant fuzzy environment, and the recent developments of some related theory and techniques.- Chapter 2 This chapter introduces the TODIM decision making method based on the hesitant fuzzy psychological distance measure.- Chapter 3 This chapter introduces a process-oriented dynamic decision making method based on the hesitant fuzzy decision field theory.- Chapter 4 A dynamic uncertain reasoning method based on the Dynamic Hesitant Fuzzy Bayesian Network is introduced.- Chapter 5 Two regression analysis models under the hesitant fuzzy environment are introduced.- Chapter 6 A cluster algorithm with probabilistic hesitant fuzzy correlation coefficient and the concept of interval-valued probabilistic hesitant fuzzy set are introduced to improve the theory of hesitant fuzzy set.