Evolutionary q-Gaussian Radial Basis Functions for Binary-Classification
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.
KeywordsHide Layer Radial Basis Function Hide Node Hybrid Algorithm Radial Basis Function Neural Network
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- 7.Asuncion, A., Newman, D.: UCI machine learning repository (2007)Google Scholar