Skip to main content
SpringerLink
Log in
Book cover

Soft Computing for Business Intelligence pp 267–280Cite as

Type-2 Fuzzy Logic in Decision Support Systems

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 537))

Abstract

Decision Support Systems have been widely used in expert knowledge modeling. One of the known implementation approaches is through definition of Fuzzy Sets and Fuzzy Predicates, whose evaluation determines the system’s output. Despite Type-1 Fuzzy Sets have been widely used in this type of implementation, there are uncertainty sources that cannot be adequately modeled when using expert knowledge minimizing their effect on system’s output, especially when it comes from several experts opinions. Type-2 Fuzzy Sets deal with fuzzy membership degrees, which can represent adequately the typical uncertainties of these systems. In this chapter, we generalize the operators of Fuzzy Logic in order to evaluate Fuzzy Predicates with Type-2 Fuzzy Sets and we define measures to assess the degree of truth of these predicates to define the theoretical background of the Decision Support Systems using this methodology. We present an example application of decision-making and a brief discussion of the results.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andrade, R.E., Gómez, J.M., Téllez, G.M., González, E.F.: Compensatory logic: A fuzzy approach to decision making. In: Proceedings of 4th International Symposium on Engineering of Intelligent Systems (EIS 2004), Madeira, Portugal (2004)

    Google Scholar 

  2. Andrade, R.E., González, E.F.: La Lógica Difusa Compensatoria: Una Plataforma para el Razonamiento y la Representación del Conocimiento en un Ambiente de Decisión Multicriterio, Análisis Multicriterio para la Toma de Decisiones: Métodos y Aplicaciones (2009)

    Google Scholar 

  3. Andrade, R.E., Téllez, G.M., González, E.F., Marx-Gómez, J., Lecich, M.I.: Compen-satory logic: A fuzzy normative model for decision making. Investigación Operativa 27, 188–197 (2006)

    Google Scholar 

  4. Andrade, R.E., Téllez, G.M.: Consideraciones sobre el carácter normativo de la lógica difusa compensatoria. Infraestructuras de Datos Espaciales en Iberoamérica y el Caribe, Cuba (2007)

    Google Scholar 

  5. Batyrshin, I.Z., Kaynak, O., Rudas, I.: Generalizad conjuntion and disjunction operations for fuzzy control. In: EUFIT 1998, Aachen, Germany (1998)

    Google Scholar 

  6. Bouchet, A., Pastore, J., Andrade, R.E., Brun, M., Ballarin, V.: Arithmetic Mean Based Compensatory Fuzzy Logic. International Journal of Computational Intelligence and Applications 10, 231–243 (2011)

    Article  MATH  Google Scholar 

  7. Comas, D.S., Meschino, G.J., Pastore, J.I., Ballarin, V.L.: A survey of medical images and signal processing problems successfully solved with the application of Type-2 Fuzzy Logic. Journal of Physics: Conference Series (2011)

    Google Scholar 

  8. DNPC/MJDDHH: Dirección Nacional de Política Criminal. Ministerio de Justicia y Derechos Humanos, DNPC/MJDDHH (2011), http://www.jus.gob.ar/ (accessed March 26, 2012)

  9. Drewniak, J., Król, A.: A survey of weak connectives and the preservation of their properties by aggregations. Fuzzy Sets and Systems, 202–215 (2010)

    Google Scholar 

  10. Dubois, H., Prade, D.: Fuzzy Sets and Systems: Theory and Applications. Academic Press Inc., New York (1980)

    MATH  Google Scholar 

  11. Garibaldi, J.M., Ozen, T.: Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making. IEEE Transactions on Fuzzy Systems 15, 16–30 (2007)

    Article  Google Scholar 

  12. INDEC: Base de Microdatos - Encuesta Permanente de Hogares. EPH-INDEC (2011), http://www.indec.mecon.ar/ (accessed March 26, 2012)

  13. John, R.I., Innocent, P.R., Barnes, M.R.: Type 2 fuzzy sets and neuro-fuzzy clustering of radiographic tibia images. In: Proceedings of the Sixth IEEE International Conference on Computational Intelligence, Anchorage, AK, USA (1998)

    Google Scholar 

  14. Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 Fuzzy Logic Systems. IEEE Transaction on Fuzzy Systems 7, 643–658 (1999)

    Article  Google Scholar 

  15. Karnik, N.N., Mendel, J.M.: Operations on Type-2 Fuzzy Sets. Fuzzy Sets and Systems 122, 327–348 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  16. Li, H.X., Yen, V.C.: Fuzzy Sets and Fuzzy Decision-Making. N.W. Boca Raton (1995)

    Google Scholar 

  17. Liang, Q., Mendel, J.M.: Interval Type-2 Fuzzy Logic Systems: Theory and Design. IEEE Transaction on Fuzzy Systems 8, 535–550 (2000)

    Article  Google Scholar 

  18. Mendel, J., John, R.I.B.: Type-2 Fuzzy Sets Made Simple. IEEE Transactions on Fuzzy Systems 10, 117–127 (2002)

    Article  Google Scholar 

  19. Mendel, J.: Fuzzy sets for words: a new beginning. In: 12th IEEE International Conference on Fuzzy Systems, Saint Louis, MO (2003)

    Google Scholar 

  20. Mendel, J.M.: Type-2 fuzzy sets and systems: an overview. IEEE Computational Intelligence Magazine 2, 20–29 (2007)

    Google Scholar 

  21. Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper-Saddle River (2001)

    Google Scholar 

  22. Mendel, J.M.: Uncertainty, fuzzy logic, and signal processing. Signal Processing, 913–933 (2000)

    Google Scholar 

  23. Mizumoto, M., Tanaka, K.: Some Properties of Fuzzy Sets of Type 2. Information and Control 31, 312–340 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  24. PNUD: Aportes para el Desarrollo Humano 2002. PNUD, Buenos Aires (2002)

    Google Scholar 

  25. PNUD: Aportes para el Desarrollo Humano en Argentina 2009. PNUD, Buenos Aires (2009)

    Google Scholar 

  26. Wagner, C., Hagras, H.: Uncertainty and Type-2 Fuzzy Sets and Systems. In: UK Workshop on Computational Intelligence (UKCI), Colchester, UK (2010)

    Google Scholar 

  27. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  28. Zadeh, L.A.: The Concept of a Linguistic Variable and its Application to Approximate Reasoning-I. Information Sciences, 199–249 (1975)

    Google Scholar 

  29. Zarandi, M.H., Zarinbal, M., Izadi, M.: Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach. Applied Soft Computing 11, 285–294 (2011)

    Article  Google Scholar 

  30. Zeng, J., Liu, Z.-Q.: Type-2 fuzzy hidden Markov models and their application to speech recognition. IEEE Transaction on Fuzzy Systems 14, 454–467 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Grupo de Procesamiento Digital de Imágenes, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina

    Diego S. Comas, Juan I. Pastore, Agustina Bouchet, Virginia L. Ballarin & Gustavo J. Meschino

  2. Consejo Nacional de Investigaciones Científicas y Técnicas, CONICET, Argentina, Buenos Aires

    Diego S. Comas, Juan I. Pastore & Agustina Bouchet

Authors
  1. Diego S. Comas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan I. Pastore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Agustina Bouchet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Virginia L. Ballarin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gustavo J. Meschino
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diego S. Comas .

Editor information

Editors and Affiliations

  1. Centro de Estudios de Técnicas de Dirección, Instituto Superior Politécnico José Antonio Echeverría, La Habana, Cuba

    Rafael Espin

  2. Centro de Estudios de Informática Carretera a Camajuani, Universidad Central Marta Abreu de Las Villas, Santa Clara, Cuba

    Rafael Bello Pérez

  3. Department of Applied Mathematics and Computer Science, Universidad de Cantabria, Santander, Spain

    Angel Cobo

  4. Department für Informatik, Carl Von Ossietzky Universität Oldenburg, Oldenburg, Germany

    Jorge Marx

  5. Instituto Superior Politécnico José Antonio Echeverría, La Habana, Cuba

    Ariel Racet Valdés

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Comas, D.S., Pastore, J.I., Bouchet, A., Ballarin, V.L., Meschino, G.J. (2014). Type-2 Fuzzy Logic in Decision Support Systems. In: Espin, R., Pérez, R., Cobo, A., Marx, J., Valdés, A. (eds) Soft Computing for Business Intelligence. Studies in Computational Intelligence, vol 537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53737-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-53737-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53736-3

  • Online ISBN: 978-3-642-53737-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation