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Fuzzy Logic for Stochastic Modeling

  • Chapter
Soft Methods for Integrated Uncertainty Modelling

Part of the book series: Advances in Soft Computing ((AINSC,volume 37))

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Abstract

Exploring the growing interest in extending the theory of probability and statistics to allow for more flexible modeling of uncertainty, ignorance, and fuzziness, the properties of fuzzy modeling are investigated for statistical signals, which benefit from the properties of fuzzy modeling. There is relatively research in the area, making explicit identification of statistical/stochastic fuzzy modeling properties, where statistical/stochastic signals are in play. This research makes explicit comparative investigations and positions fuzzy modeling in the statistical signal processing domain, next to nonlinear dynamic system modeling.

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References

  1. Zadeh LA (1996) Fuzzy logic=computing with words. IEEE Trans. on Fuzzy Systems 4(2):103–111

    Article  MathSciNet  Google Scholar 

  2. Casillas J, Cordon O, Herrara F, Magdalena L (eds.) (2003) Interpretability Issues in Fuzzy Modeling, Springer

    Google Scholar 

  3. Mamdani EH (1974) Applications of fuzzy algorithms for control of a simple dynamic plant. In: Proceedings of the IEE, 121, 12:1585–1588

    Google Scholar 

  4. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans. On Systems, Man, and Cybernetics 15:116–132

    MATH  Google Scholar 

  5. Bezdek JC (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, NY

    Google Scholar 

  6. Gustafson DE Kessel WC (1979) Fuzzy clustering with a fuzzy covariance matrix. In: Proc. IEEE CDC, San Diego, CA, pp 761–766

    Google Scholar 

  7. Kruse R, Gebhardt J, Klawonn F (1994) Foundations of Fuzzy Systems. Wiley, NY

    Google Scholar 

  8. Ciftcioglu Ö, Bittermann MS, Sariyildiz IS, (2006) Studies on visual perception for perceptual robotics. In: ICINCO 2006 3rd Int. Conference on Informatics in Control, Automation and Robotics, August 1–5, Setubal, Portugal

    Google Scholar 

  9. Ciftcioglu Ö (2006) On the efficiency of multivariable TS fuzzy modeling. In: Proc. FUZZ-IEEE 2006, World Congress on Computational Intelligence, July 16–21, 2006, Vancouver, Canada

    Google Scholar 

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Author information

Authors and Affiliations

  1. Delft University of Technology, Berlageweg 1, 2628 CR Delft, The Netherlands

    Özer Ciftcioglu

Authors
  1. Özer Ciftcioglu
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  2. I. Sevil Sariyildiz
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Editor information

Editors and Affiliations

  1. AI Group Department of Engineering Mathematics, University of Bristol, BS8 1TR, Bristol, UK

    Jonathan Lawry

  2. Statistics and Operations Research, Rey Juan Carlos University, C-Tulip˙n s/n MÛstoles28933, Spain

    Enrique Miranda

  3. Intelligent Systems Group Department of Electronics & Computer Science, University of Santiago de Compostela Santiago de Compostela, Spain

    Alberto Bugarin

  4. Department of Applied Mathematics, Beijing University of Technology, 100022, Beijing, P.R. China

    Shoumei Li

  5. Dpto. Estadistica e I.O y D.M. Calle Calvo Sotelo s/n, Universiad de Oviedo Fac. Ciencias, 33071, Oviedo, Spain

    Maria Angeles Gil

  6. Systems Research Institute Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Przemys aw Grzegorzewski

  7. Systems Research Institute Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Olgierd Hyrniewicz

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© 2006 Springer

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Ciftcioglu, Ö., Sariyildiz, I.S. (2006). Fuzzy Logic for Stochastic Modeling. In: Lawry, J., et al. Soft Methods for Integrated Uncertainty Modelling. Advances in Soft Computing, vol 37. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-34777-1_41

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  • DOI: https://doi.org/10.1007/3-540-34777-1_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34776-7

  • Online ISBN: 978-3-540-34777-4

  • eBook Packages: EngineeringEngineering (R0)

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