Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Cognitive Computation
Erschienen in: Cognitive Computation 1/2017

16.12.2016

Distance and Aggregation-Based Methodologies for Hesitant Fuzzy Decision Making

verfasst von: B. Farhadinia, Zeshui Xu

Erschienen in: Cognitive Computation | Ausgabe 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Hesitant fuzzy set (HFS) as an effective tool to reflect human’s hesitancy has received great attention in recent years. The importance weights of possible values in hesitant fuzzy elements (HFEs), which are the basic units of a HFS, have not been taken into account in the existing literature. Thus, the frequently used HFEs cannot deal with the situations where all the possible values are provided by experts with different levels of expertise. Consequently, in this paper, we propose an extension of typical HFS called the ordered weighted hesitant fuzzy set (OWHFS). The basic units of an OWHFS allow the membership of a given element to be defined in terms of several possible values together with their importance weights. Moreover, in order to indicate that the OWHFS has a good performance in decision making, we first present some information measures and several aggregation operators for OWHFSs. Then, we apply them to multi-attribute decision making with ordered weighted hesitant fuzzy information.
Vorheriger Artikel H-MRST: A Novel Framework For Supporting Probability Degree Range Query Using Extreme Learning Machine
Nächster Artikel Learning Tone Mapping Function for Dehazing

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Akusok A, Miche Y, Hegedus J, Nian R, Lendasse A. A two stage methodology using K-NN and false-positive minimizing ELM for nominal data classification. Cogn Comput 2014;6:432–445.CrossRef
2.
Atanassov K. Intuitionistic Fuzzy Sets: Theory and Applications, Physica-Verlag. Heidelberg: NewYork; 1999.CrossRef
3.
Atanassov K. Intuitionistic fuzzy sets. Fuzzy Sets Syst 1986;20:87–96.CrossRef
4.
Atanassov K, Gargov G. Interval-valued intuitionistic fuzzy sets. Fuzzy Sets Syst 1989;31:343–349.CrossRef
5.
Bustince H. Indicator of inclusion grade for interval-valued fuzzy sets: application to approximate reasoning based on interval-valued fuzzy sets. Int J Approx Reason 2000;23(3):137–209.CrossRef
6.
Bustince H, Burillo P. Vague sets are intuitionistic fuzzy sets. Fuzzy Sets Syst 1996;79:403–405.CrossRef
7.
Czubenko M, Kowalczuk Z, Ordys A. Autonomous driver based on an intelligent system of decision-making. Cogn Comput 2015;7:569–581.CrossRef
8.
Dubois D, Prade H. Fuzzy Sets and Systems Theory and Applications. New York: Kluwer Academic; 1980.
9.
Farhadinia B. A novel method of ranking hesitant fuzzy values for multiple attribute decision-making problems. Int J Intell Syst 2013;28:752–767.CrossRef
10.
Farhadinia B. Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets. Inf Sci 2013; 240:129–144.CrossRef
11.
Farhadinia B. Distance and similarity measures for higher order hesitant fuzzy sets. Knowl-Based Syst 2014; 55:43–48.CrossRef
12.
Farhadinia B. Correlation for dual hesitant fuzzy sets and dual interval-valued hesitant fuzzy sets. Int J Intell Syst 2014;29:184–205.CrossRef
13.
Farhadinia B. A series of score functions for hesitant fuzzy sets. Inf Sci 2014;277:102–110.CrossRef
14.
Farhadinia B. Multi criteria decision making method based on the higher order hesitant fuzzy soft set. Int Scholar Research Notices 2014. Hindawi.
15.
Farhadinia B. Hesitant fuzzy set lexicographical ordering and its application to multi-attribute decision making. Inf Sci 2016;327:233–245.CrossRef
16.
Farhadinia B. Multiple criteria decision making methods with completely unknown weights in hesitant fuzzy linguistic term setting. Knowl-Based Syst 2016;93:135–144.CrossRef
17.
Rodriguez RM, Bedregal B, Bustince H, Dong YC, Farhadinia B, Kahraman C, Martinez L, Torra V, Xu YJ, Xu ZS, Herrera F. A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress. Inf Fusion 2016;29:89–97.CrossRef
18.
19.
Gorzalczany MB. A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst 1987;21:1–17.CrossRef
20.
Kahraman C, Kaya I. A fuzzy multicriteria methodology for selection among energy alternatives. Expert Syst Appl 2010;37:6270–6281.CrossRef
21.
Klir GJ, Yuan B. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Upper Saddle River: Prentice-Hall; 1995.
22.
Meng FY, Chen XH. Correlation coefficients of hesitant fuzzy sets and their application based on fuzzy measures. Cogn Comput 2015;7:445–463.CrossRef
23.
Meng FY, Wang C, Chen XH. Linguistic interval hesitant fuzzy sets and their application in decision making. Cogn Comput 2016;8:52–68.CrossRef
24.
Mitchell HB. Pattern recognition using type-II fuzzy sets. Inf Sci 2005;2-4:409–418.CrossRef
25.
Miyamoto S. Fuzzy multisets and their generalizations. Multiset Processing 2001;2235:225–235.CrossRef
26.
Mizumoto M, Tanaka K. Some properties of fuzzy sets of type-2. Inf Control 1976;31:312–340.CrossRef
27.
Torra V. Hesitant fuzzy sets. Int J Intell Syst 2010;25:529– 539.
28.
Peng DH, Gao CY, Gao ZF. Generalized hesitant fuzzy synergetic weighted distance measures and their application to multiple criteria decision making. Appl Math Model 2013;37:5837– 5850.CrossRef
29.
Rodriguez RM, Martinez L, Herrera F. Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Syst 2012;20:109–119.
30.
Turksen IB. Interval valued fuzzy sets based on normal forms. Fuzzy Sets Syst 1986;20:191–210.CrossRef
31.
Wei GW. Hesitant fuzzy prioritized operators and their application to multiple attribute decision making. Knowl-Based Syst 2012;31:176–182.CrossRef
32.
Wei GW, Lan G. Grey relational analysis method for interval-valued intuitionistic fuzzy multiple attribute decision making. 5th International Conference on Fuzzy Systems and Knowledge Discovery; 2008. p. 291–295.
33.
Wei GW, Zhao XF, Lin v. Some hesitant interval-valued aggregation operators and their applications to multiple attribute decision making. Knowl-Based Syst 2013;46:43–53.CrossRef
34.
Wu D, Mendel JM. A vector similarity measure for linguistic approximation: interval type-2 and type-1 fuzzy sets. Inf Sci 2008;178(2):381–402.CrossRef
35.
Xia MM, Xu ZS. Hesitant fuzzy information aggregation in decision making. Int J Approx Reason 2011; 52:395–407.CrossRef
36.
Xu ZS. Deviation measures of linguistic preference relations in group decision making. Omega 2005;17:432–445.
37.
Xu ZS, Xia MM. Distance and similarity measures for hesitant fuzzy sets. Inf Sci 2011;181:2128–2138.CrossRef
38.
Yager RR. On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 1988;18:183–190.CrossRef
39.
Yang J, Gong LY, Tang YF, Yan J, He HB, Zhang LQ, Li G. An improved SVM-based cognitive diagnosis algorithm for operation states of distribution grid. Cogn Comput 2015;7:582–593.CrossRef
40.
Yu DJ. Some hesitant fuzzy information aggregation operators based on Einstein operational laws. Int J Intell Syst 2014;00:1– 21.
41.
Zadeh LA. Fuzzy sets. Inf Comput 1965;8:338–353.
42.
Zhang Z, Wu C. Weighted hesitant fuzzy sets and their application to multi-criteria decision making. British J Math Comput Sci 2014;4:1091–1123.CrossRef
43.
Tian ZP, Wang J, Wang JQ, Zhang HY. A likelihood-based qualitative flexible approach with hesitant fuzzy linguistic information. Cogn Comput 2016. doi:10.​1007/​s12559-016-9400-1.
44.
Zhu B, Xu ZS. Hesitant fuzzy Bonferroni means for multi-criteria decision making. J Oper Res Soc 2013; 64:1831–1840.CrossRef
45.
Zhu B, Xu ZS, Xia MM. Dual hesitant fuzzy sets. J Appl Math 2012;2012:1–13.
46.
Zhu B, Xu ZS, Xia MM. Hesitant fuzzy geometric Bonferroni means. Inf Sci 2012:72–85.
Metadaten
Titel
Distance and Aggregation-Based Methodologies for Hesitant Fuzzy Decision Making
verfasst von
B. Farhadinia
Zeshui Xu
Publikationsdatum
16.12.2016
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 1/2017
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-016-9436-2

Weitere Artikel der Ausgabe 1/2017

Cognitive Computation 1/2017 Zur Ausgabe

OriginalPaper

On Global Smooth Path Planning for Mobile Robots using a Novel Multimodal Delayed PSO Algorithm

OriginalPaper

A Digital Communication Analysis of Gene Expression of Proteins in Biological Systems: A Layered Network Model View

OriginalPaper

A Semi-Supervised Predictive Sparse Decomposition Based on Task-Driven Dictionary Learning

OriginalPaper

Semi-supervised Learning for Affective Common-Sense Reasoning

BriefCommunication

Anatomy of the Mind: a Quick Overview

OriginalPaper

Human Brain Function in Path Planning: a Task Study