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.
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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
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