Abstract
Initially, it is shown that ε-insensitive learning of a fuzzy system may be presented as a combination of both an ε-insensitive gradient method and solving a system of linear inequalities. Then, a hybrid learning algorithm is introduced. Example is given of using this algorithm for design a fuzzy model of real ECG data. Simulation results show an improvement in the generalization ability of a fuzzy system learned by the new method with respect to the traditional and other ε-insensitive learning methods.
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© 2005 Springer-Verlag Berlin Heidelberg
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Czogala, T., Leski, J.M. (2005). A Hybrid ε-Insensitive Learning of Fuzzy Systems. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_15
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DOI: https://doi.org/10.1007/3-540-32390-2_15
Publisher Name: Springer, Berlin, Heidelberg
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