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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
References
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)
Mitra, S., Pal, S.K.: Logical Operation Based Fuzzy MLP for Classification and Rule Generation. Neural Networks 7(2), 683–697 (1994)
Banerjee, M., Mitra, S., Pal, S.K.: Rough Fuzzy MLP: Knowledge Encoding and Classification. IEEE Transactions On Neural Networks 9(6), 1203–1216 (1998)
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)
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)
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)
Schweizer, B., Sklar, M.: Associative Functions and Statistical Inequalities. Publ. Math. Debrecen 8(1), 169–186 (1961)
Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, New Jersey (1988)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, New Jersey (1995)
Pal, S.K., Mitra, S.: Multilayer Perceptron, Fuzzy Sets, and Classification. IEEE Transactions on Neural Networks 3(5), 683–697 (1992)
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)
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)
Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
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)
Pedrycz, W., Gomide, F.: An Introduction to Fuzzy Sets - Analysis and Designs, 1st edn. MIT Press, Cambridge (1994)
Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining. Chapman/Hall-CRC, New York (2004)
Nauck, D., Klawonn, F., Kruse, R.: Neuro-Fuzzy Systems. John Wiley and Sons, New York (1997)
Indian Statistical Institute, Calcutta, http://www.isical.ac.in/~miu
Fisher, R.A. (1936), http://archive.ics.uci.edu/ml/datasets/Iris
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-88309-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88308-1
Online ISBN: 978-3-540-88309-8
eBook Packages: Computer ScienceComputer Science (R0)