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Ibero-American Conference on Artificial Intelligence

IBERAMIA 2008: Advances in Artificial Intelligence – IBERAMIA 2008 pp 123–132Cite as

Rough Evolutionary Fuzzy System Based on Interactive T-Norms

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5290)

Abstract

A rough evolutionary neuro-fuzzy system for classification and rule generation is proposed. Interactive and differentiable t-norms and t-conorms involving logical neurons in a three-layer perceptron are used. This paper presents the results of application of the methodology based on rough set theory, which initializes the number of hidden nodes and some of the weight values. In search of the smallest network with a good generalization capacity, the genetic algorithms operate on population of individuals composed by integration of dependency rules that will be mapped on networks. Justification of an inferred decision was produced in rule form expressed as the disjunction of conjunctive clauses. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of fuzzy-MLP and Rough-Fuzzy-MLP, with no logical neuron; the Logical-P, which uses product and probabilistic sum; and other related models.

Keywords

  • T-norms
  • Classification
  • Rule Generation
  • Hybrid System

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  • DOI: 10.1007/978-3-540-88309-8_13
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References

  1. Klösgen, W., Zytkow, J.M.: Handbook of Data Mining and Knowledge Discovery. Cap. 10 by Witold Pedrycz, 1st edn. Oxford University Press, New York (2002)

    Google Scholar 

  2. Mitra, S., Pal, S.K.: Logical Operation Based Fuzzy MLP for Classification and Rule Generation. Neural Networks 7(2), 683–697 (1994)

    CrossRef  Google Scholar 

  3. Banerjee, M., Mitra, S., Pal, S.K.: Rough Fuzzy MLP: Knowledge Encoding and Classification. IEEE Transactions On Neural Networks 9(6), 1203–1216 (1998)

    CrossRef  Google Scholar 

  4. Zanusso, M.B.: Familias de T-Normas Diferenciáveis, Funções de Pertinência Relacionadas e Aplicações. Universidade Federal de Santa Catarina. Tese de Doutorado, Brasil (November 1997)

    Google Scholar 

  5. Oliveira, F.R., Zanusso, M.B.: A Fuzzy Neural Network with Differentiable T-Norms: Classification and Rule Generation. In: International Conference on Artificial Intelligence(ICAI), Las Vegas, Nevada, June 27-30, vol. I(1), pp. 195–201 (2005)

    Google Scholar 

  6. Oliveira, F.R.: Rede Neural Difusa com T-normas Diferenciáveis e Interativas. Universidade Federal de Mato Grosso do Sul, Dissertação de Mestrado, Brasil, Novembro (2006)

    Google Scholar 

  7. Schweizer, B., Sklar, M.: Associative Functions and Statistical Inequalities. Publ. Math. Debrecen 8(1), 169–186 (1961)

    MATH  MathSciNet  Google Scholar 

  8. Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, New Jersey (1988)

    MATH  Google Scholar 

  9. Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, New Jersey (1995)

    MATH  Google Scholar 

  10. Pal, S.K., Mitra, S.: Multilayer Perceptron, Fuzzy Sets, and Classification. IEEE Transactions on Neural Networks 3(5), 683–697 (1992)

    CrossRef  Google Scholar 

  11. Lovón, G.L.M.: Rough Sets e Algoritmo Genético para Inicializar um Sistema Neuro-Fuzzy. Universidade Federal de Mato Grosso do Sul. Dissertação de Mestrado, Brasil (April 2007)

    Google Scholar 

  12. Lovón, G.L.M., Zanusso, M.B.: Rough Sets e Algoritmo Genético para Inicializar um Sistema Neuro-Fuzzy. In: XXXIII Conferencia Latinoamericana en Informática, San José, Costa Rica (2007)

    Google Scholar 

  13. Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  14. Zanusso, M.B., Araújo, A.: Differentiable T-Norms and Related Membership Functions Families and their Applications. In: IBERAMIA-SBIA, November 19-22, vol. 2, pp. 294–303 (2000)

    Google Scholar 

  15. Pedrycz, W., Gomide, F.: An Introduction to Fuzzy Sets - Analysis and Designs, 1st edn. MIT Press, Cambridge (1994)

    Google Scholar 

  16. Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining. Chapman/Hall-CRC, New York (2004)

    MATH  Google Scholar 

  17. Nauck, D., Klawonn, F., Kruse, R.: Neuro-Fuzzy Systems. John Wiley and Sons, New York (1997)

    Google Scholar 

  18. Indian Statistical Institute, Calcutta, http://www.isical.ac.in/~miu

  19. Fisher, R.A. (1936), http://archive.ics.uci.edu/ml/datasets/Iris

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Author information

Authors and Affiliations

  1. Departamento de Computação e Estatística, Universidade Federal do Mato Grosso do Sul, Campo Grande, MS, Brasil

    Graciela L. Meza Lovón & Maria Bernadete Zanusso

Authors
  1. Graciela L. Meza Lovón
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  2. Maria Bernadete Zanusso
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Editor information

Editors and Affiliations

  1. ICREA & Universitat Pompeu Fabra, Paseo de Circumvalacion 8, 08003, Barcelona, Spain

    Hector Geffner

  2. IST-UTL and INESC-ID, Av. Prof. Cavaco Silva - Taguspark, 2744-016, Porto Salvo, Portugal

    Rui Prada

  3. ADETTI/ISCTE and ISCTE, Lisbon University Institute, Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    Isabel Machado Alexandre

  4. ADETTI/ISCTE and ISCTE, Lisbon University Institute, , Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    Nuno David

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Lovón, G.L.M., Zanusso, M.B. (2008). Rough Evolutionary Fuzzy System Based on Interactive T-Norms. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_13

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  • DOI: https://doi.org/10.1007/978-3-540-88309-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

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