Fuzzy Hyperinference-Based Pattern Recognition

  • Mario Rosario GuarracinoEmail author
  • Raimundas Jasinevicius
  • Radvile Krusinskiene
  • Vytautas Petrauskas
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)


The paper presents a new approach to the problem of pattern recognition. First of all, here is emphasized that the problem itself is fuzzy enough. Later three following novelties of the approach are disclosed: 1) the rule-based fuzzy inference, concerning the measure of patterns’ similarity, is enriched by an idea of hyperinference; 2) a description of the main pattern recognition process is based on Takagi-Sugeno (T-S) reasoning procedure and 3) rule weights in T-S procedure are defined, solving special linear or piecewise linear programming problem (LPP or PWLPP), constructed according to the certain fuzzy experts’ information. The proposed approach was used successfully for recognition of healthy people and those who suffer from certain illness (for example, an atherosclerosis). The classification was performed according to person’s clinical posturograms (stabilograms). At the end of this paper experimental results are presented as well as acknowledgement to all anonymous participants of the experiments.


Fuzzy Rule Pattern Recognition Problem Rule Weight Reasoning Procedure Anonymous Participant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. J. Wiley & Sons, New York (2001)zbMATHGoogle Scholar
  2. 2.
    Konar, A.: Computational Intelligence (Principles, Techniques and Applications). Springer, Heidelberg (2005)zbMATHGoogle Scholar
  3. 3.
    Mangasarian, O.L.: Operations Research 13, 444–452 (1965)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Guarracino, M.R., Cifarelli, C., Seref, O., Pardalos, P.: Optimization Methods and Software 22(1), 73–81 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Kiendl, H., Knicker, R., Niewels, F.: Two-way fuzzy controllers based on hyperinference and inference filter. In: Proc. 2nd World Automation Congress, Intelligent Automation and Control, vol. 4, pp. 387–394. TSI Press, Montpellier (1996)Google Scholar
  6. 6.
    Krone, A., Schwane, U.: Generating fuzzy rules from contradictory data of different control strategies and control performances. In: Proc. 5th IEEE Int. Conf. on Fuzzy Systems, New Orleans, pp. 492–497 (1996)Google Scholar
  7. 7.
    Kosko, B.: Fuzzy Engineering. Prentice Hall (1997)Google Scholar
  8. 8.
    Jasinevicius, R.: Parallel Space-Time Computing Structures, Mokslas, Vilnius (1988) (in Russian)Google Scholar
  9. 9.
    Jasinevicius, R., Petrauskas, V.: Fuzzy expert maps: the new approach. In: Proc. IEEE Congress on Evolutionary Computation. IEEE Press, Piscataway (2008)Google Scholar
  10. 10.
    Barauskas, R., Krusinskiene, R.: Journal of Sound and Vibration 308, 612–624 (2007)CrossRefGoogle Scholar
  11. 11.
    Juodzbaliene, V., Muckus, K., Krisciukaitis, A.: Acta Kinesiologiae Universitatis Tartuensis 7, 89–93 (2002)Google Scholar
  12. 12.
    Grabauskas, V.: Medicinos enciklopedija, vol. 1. Enciklopedij Leidykla, Vilnius (1991)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Mario Rosario Guarracino
    • 1
    Email author
  • Raimundas Jasinevicius
    • 2
  • Radvile Krusinskiene
    • 2
  • Vytautas Petrauskas
    • 2
  1. 1.Institute for High Performance Computing and NetworkingNational Research Council of ItalyNapoliItaly
  2. 2.Department of InformaticsKaunas University of TechnologyKaunasLithuania

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