Advertisement

Informatik-Spektrum

, Volume 38, Issue 6, pp 543–549 | Cite as

Von Fuzzy-Sets zu Computing-with-Words

  • Sara D’OnofrioEmail author
  • Edy Portmann
HAUPTBEITRAG VON FUZZY-SETS ZU COMPUTING-WITH-WORDS

Zusammenfassung

Dieser Artikel bietet einen Überblick über die Entwicklung und Zusammenhänge der einzelnen Elemente der Fuzzy-Logik, wovon Fuzzy-Set-Theorie die Grundlage bildet. Die Grundproblematik besteht in der Handhabung von linguistischen Informationen, die häufig durch Ungenauigkeit gekennzeichnet sind. Die verschiedenen technischen Anwendungen von Fuzzy-Logik bieten eine Möglichkeit, intelligentere Computersysteme zu konstruieren, die mit unpräzisen Informationen umgehen können. Solche Systeme sind Indizien für die Entstehung einer neuen Ära des Cognitive-Computing, die in diesem Artikel ebenfalls zur Sprache kommt. Für das bessere Verständnis wird der Artikel mit einem Beispiel aus der Meteorologie (d. h. Schnee in Adelboden) begleitet.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Badredine A (2005) Fuzzy decision making in politics: a linguistic fuzzy set approach (LFSA). Pol Anal 13(1):23–56CrossRefGoogle Scholar
  2. 2.
    Bar-Cohen Y (2006) Biomimetics – using nature to inspire human innovation. Bioinspiration Biomimetics 1(1):1–12CrossRefGoogle Scholar
  3. 3.
    Berners-Lee T, Hendler J, Lassila O (2001) The Semantic Web. Sci Am 29–37Google Scholar
  4. 4.
    Dernoncourt F (2011) Introduction to fuzzy logic. MITGoogle Scholar
  5. 5.
    Dubois D, Prade H (1998) An introduction to fuzzy systems. Clin Chim Acta 270(1):3–29CrossRefGoogle Scholar
  6. 6.
    Froese N. Aristoteles: Logik und Methodik in der Antike. Logische Grundprinzipien, der Syllogismus und antike Wissenschaftsphilosophie. http://www.antike-griechische.de/Aristoteles.pdf, letzter Zugriff: 15.09.2015Google Scholar
  7. 7.
    Haun M (2014) Cognitive Computing. Steigerung des systemischen Intelligenzprofils. Springer, Berlin HeidelbergGoogle Scholar
  8. 8.
    Herrera F, Martinez L (2000) A 2-tuple fuzzy linguistic representation, model for computing with words. IEEE T Fuzzy Syst 8(6):746–752CrossRefMathSciNetGoogle Scholar
  9. 9.
    Hobbs JR (1985) Granularity. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Los Angeles, CA, pp 432–435Google Scholar
  10. 10.
    Jafar OAM, Sivakumar R (2013) A Comparative Study of Hard and Fuzzy Data Clustering Algorithms with Cluster Validity Indices. In: Proceedings of International Conference on ,,Emerging Research in Computing, Information, Communication and Applications (ERCICA 2013)“, Elsevier Publications, pp 775–782Google Scholar
  11. 11.
    Kaufmann M, Portmann E, Fathi M (2012) A concept of semantics extraction from web data by induction of fuzzy ontologies. In: International Workshop on Uncertainty Reasoning for the Semantic WebGoogle Scholar
  12. 12.
    Kaufmann M, Portmann E (2015) Biomimetics in design-oriented information systems research. In: Donnellan B, Gleasure R, Helfert M, Kenneally J, Rothenberger M, Chiarini Tremblay M, Vandermeert D, Winter R (eds) At the Vanguard of Design Science: First Impressions and Early Finding from Ongoing Research Research-in-Progress Papers and Poster Presentations from the 10th International Conference, DESRIST, Dublin, Ireland, pp 53–60Google Scholar
  13. 13.
    Klir GJ, Yuan B (1995) Fuzzy Sets and Fuzzy Logic – Theory and Applications. Prentice-Hall, New YorkzbMATHGoogle Scholar
  14. 14.
    Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75zbMATHCrossRefGoogle Scholar
  15. 15.
    Lawry J (2001) A methodology for computing with words. Int J Approx Reason 28(2):51–89zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Loucks DP, van Beek E, Stedinger JR, Dijkman JPM, Viallers MT (2005) Water Resources Systems Planning and Management: An Introduction to Methods, Models and Application. UNESCO, Paris, pp 135–144Google Scholar
  17. 17.
    Mendel JM (2007) Computing with words and its relationships with fuzzistics. Inform Sciences 177(4):988–1006CrossRefMathSciNetGoogle Scholar
  18. 18.
    Mendel JM, Zadeh LA, Trillas E, Yager R, Lawry J, Hagas H, Guadarrama S (2010) What computing with words means to me. IEEE Comput Intell Mag 20–26Google Scholar
  19. 19.
    Papageorgiou EI, Salmeron JL (2013) A review of fuzzy cognitive maps research during the last decade. IEEE T Fuzzy Syst 21(1):66–79CrossRefGoogle Scholar
  20. 20.
    Pedrycz W (2010) The design of cognitive maps: a study in synergy of granular computing and evolutionary optimization. Expert Syst Appl 37(10):7288–7294CrossRefGoogle Scholar
  21. 21.
    Pedrycz W, Jastrzebska A, Homenda W (2015) Design of fuzzy cognitive maps for modeling time series. IEEE T Fuzzy SystGoogle Scholar
  22. 22.
    Portmann E, Kaufmann MA, Graf C (2012) A distributed, semiotic-inductive, and human-oriented approach to web-scale knowledge retrieval. In: Proceedings of the 2012 International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning, ACM, New York, pp 1–8Google Scholar
  23. 23.
    Rappaport WJ (2003) What did you mean by that? Misunderstanding, negotiation, and syntactic semantics. Mind Mach 13:397–427CrossRefMathSciNetGoogle Scholar
  24. 24.
    Reformat M, Ly C (2009) Ontological approach to development of computing with words based systems. Int J Approx Reason 50(1):72–91CrossRefGoogle Scholar
  25. 25.
    Siemens G (2005) Connectivism: a learning theory for the digital age. Int J Instr Tech Dist Learn 2(1):3–10Google Scholar
  26. 26.
    Spinas O. Zur Geschichte der Logik. https://www.math.uni-kiel.de/logik/de/arbeitsgruppe-logik/zur-geschichte-der-logik, letzter Zugriff: 1.6.2015Google Scholar
  27. 27.
    Strahm T (1999) Logik in Informatik, Mathematik und Philosophie. Vortrag anlässlich der Veranstaltung Theodor-Kocher-Preis der Universität Bern 1998Google Scholar
  28. 28.
    Tolman EC (1948) Cognitive maps in rats and men. Psychol Rev 55(4):189–208CrossRefGoogle Scholar
  29. 29.
    Wang Y (2006) Keynote speech: cognitive informatics – towards future generation computers that think and feel. In: Proceedings of the 5th IEEE International Conference on Cognitive Informatics (ICCI’06), Beijing, China, IEEE CS Press, pp 3–7Google Scholar
  30. 30.
    Yager RR, Filev D (1998) Operations for granular computing: mixing words and numbers. In: Proceedings of the FUZZ-IEEE World Congress on Computational Intelligence, Anchorage, pp 123–128Google Scholar
  31. 31.
    Yao YY (2000) Granular computing: basic issues and possible solutions. In: Proceedings of the 5th Conference on Information Sciences, Atlantic, NJ, USA, vol 1, pp 186–189Google Scholar
  32. 32.
    Yao YY (2006) Three perspectives of granular computing. In: Proceedings of the International Forum on Theory of GrC from Rough Set Perspective. J Nanchang Inst Technol 25(2):16–21Google Scholar
  33. 33.
    Ying M (2002) A formal model of computing with words. IEEE T Fuzzy Syst 10(5):640–652CrossRefGoogle Scholar
  34. 34.
    Zadeh LA (1965) Fuzzy sets. Inform Control 8:338–353zbMATHCrossRefMathSciNetGoogle Scholar
  35. 35.
    Zadeh LA(1975) The concept of a linguistic variable and its applications to approximate reasoning – I. Inform Sci 8:199–249Google Scholar
  36. 36.
    Zadeh LA (1988) Fuzzy logic. IEEE Computer 21(4):83–93CrossRefGoogle Scholar
  37. 37.
    Zadeh LA (1996) Fuzzy logic = computing with words. IEEE T Fuzzy Syst 4(2):103–111CrossRefMathSciNetGoogle Scholar
  38. 38.
    Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Set Syst 90:111–127zbMATHCrossRefMathSciNetGoogle Scholar
  39. 39.
    Zadeh LA (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput 2:23–25CrossRefGoogle Scholar
  40. 40.
    Zadeh LA (2001) From computing with numbers to computing with words – from manipulation of measurements to manipulation of perceptions. Ann NY Acad Sci 929(1):221–252CrossRefGoogle Scholar
  41. 41.
    Zadeh LA (2005) Toward a generalized constraint of uncertainty (GTU) – an outline. Inform Sciences 172:1–40zbMATHCrossRefMathSciNetGoogle Scholar
  42. 42.
    Zadeh LA (2008) Is there a need for fuzzy logic? Inform Sciences 178(13):2751–2779zbMATHCrossRefMathSciNetGoogle Scholar
  43. 43.
    Zadeh LA (2011) Computing with Words – Principal Concepts and Ideas. Studies in Fuzziness and Soft Computing. Springer, HeidelbergGoogle Scholar
  44. 44.
    Zadeh LA (2015) Fuzzy logic – a personal perspective. Fuzzy Set SystGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Institut für WirtschaftsinformatikUniversität BernBernSchweiz

Personalised recommendations