LEADER 04207nam  22006735  450 
001 9910731473603321
005 20251009101019.0
010   $a981-9931-69-X
024 7 $a10.1007/978-981-99-3169-9
035   $a(CKB)27113548000041
035   $a(MiAaPQ)EBC30603278
035   $a(Au-PeEL)EBL30603278
035   $a(DE-He213)978-981-99-3169-9
035   $a(PPN)272273589
035   $a(EXLCZ)9927113548000041
100   $a20230619d2023      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDeriving Priorities from Incomplete Fuzzy Reciprocal Preference Relations $eTheories and Methodologies /$fby Yejun Xu
205   $a1st ed. 2023.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023.
215   $a1 online resource (180 pages)
311 0 $a9789819931682 
327   $aChapter 1. Introduction -- Chapter 2. Normalizing Rank Aggregation-based Method -- Chapter 3. Eigenvector Method -- Chapter 4. Logarithmic Least Squares Method -- Chapter 5. A Chi-Square Method -- Chapter 6. A Least Deviation Method -- Chapter 7. Priorities from Fuzzy Best Worst Method Matrix -- Chapter 8. Weighted Least Square Method -- Chapter 9. Priorities from Incomplete Hesitant Fuzzy Reciprocal Preference Relations.
330   $aAs we know, multiplicative preference relations (or called pairwise comparisons in AHP) were proposed by Dr. Thomas L Saaty. One important work is to derive its priority from pairwise comparisons. It has been proposed many methods to derive priority for multiplicative preference relation. On the basis of fuzzy sets, the fuzzy reciprocal preference relation is proposed and is extended to the incomplete contexts. However, how to derive the priorities from incomplete fuzzy reciprocal preference relations is an interesting and challenging work. This book systematically presents the theories and methodologies for deriving priorities from incomplete fuzzy reciprocal preference relations. This book can be divided into three parts. In the first part, this book introduces the basic concepts of fuzzy reciprocal preference relations and incomplete fuzzy reciprocal preference relations. Then, two consistencies of complete fuzzy reciprocal preference relations are introduced: additive consistency and multiplicative consistency. Then, the relationships between the fuzzy reciprocal elements and the weights are showed. Afterward, in the second part, different priority methods are presented. The inconsistency repairing procedures are also proposed. Last, the priority method for incomplete hesitant fuzzy reciprocal preference relations is presented. This book can be used as a reference for researchers in the areas of management science, information science, systems engineering, operations research, and other relevant fields. It can also be employed as a textbook for upper-level undergraduate students and graduate students.
606   $aArtificial intelligence
606   $aComputer science
606   $aInformation modeling
606   $aMachine theory
606   $aAlgorithms
606   $aArtificial Intelligence
606   $aModels of Computation
606   $aInformation Model
606   $aFormal Languages and Automata Theory
606   $aDesign and Analysis of Algorithms
606   $aComputer Science Logic and Foundations of Programming
615  0$aArtificial intelligence.
615  0$aComputer science.
615  0$aInformation modeling.
615  0$aMachine theory.
615  0$aAlgorithms.
615 14$aArtificial Intelligence.
615 24$aModels of Computation.
615 24$aInformation Model.
615 24$aFormal Languages and Automata Theory.
615 24$aDesign and Analysis of Algorithms.
615 24$aComputer Science Logic and Foundations of Programming.
676   $a511.3223
700   $aXu$b Yejun$01368807
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910731473603321
996   $aDeriving Priorities from Incomplete Fuzzy Reciprocal Preference Relations$93394829
997   $aUNINA