Abstract
This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means a real parameter q, named q-Gaussian RBFNN. The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop + algorithm as the local improvement procedure. In order to test its overall performance, an experimental study with eleven datasets, taken from the UCI repository is presented. The RBFNN with the q-Gaussian is compared to RBFNN with Gaussian, Cauchy and Inverse Multiquadratic RBFs.
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Fernández-Navarro, F., Hervás-Martínez, C., Gutiérrez, P.A., Cruz-Ramírez, M., Carbonero-Ruz, M. (2010). Evolutionary q-Gaussian Radial Basis Functions for Binary-Classification. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_35
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DOI: https://doi.org/10.1007/978-3-642-13803-4_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13802-7
Online ISBN: 978-3-642-13803-4
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