Digital Library of the European Council for Modelling and Simulation

Title:	Growing RBF Network Models For Solving Nonlinear Approximation And Classification Problems
Authors:	Gancho Vachkov, Valentin Stoyanov, Nikolinka Christova
Published in:	(2015).ECMS 2015 Proceedings edited by: Valeri M. Mladenov, Grisha Spasov, Petia Georgieva, Galidiya Petrova, European Council for Modeling and Simulation. doi:10.7148/2015 ISBN: 978-0-9932440-0-1 29^th European Conference on Modelling and Simulation, Albena (Varna), Bulgaria, May 26^th – 29^th, 2015
Citation format:	Gancho Vachkov, Valentin Stoyanov, Nikolinka Christova (2015). Growing RBF Network Models For Solving Nonlinear Approximation And Classification Problems, ECMS 2015 Proceedings edited by: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova  European Council for Modeling and Simulation. doi:10.7148/2015-0481
DOI:	http://dx.doi.org/10.7148/2015-0481
Abstract:	In this paper a multi-step learning algorithm for creating a Growing Radial Basis Function Network (RBFN) Model is presented and analyzed. The main concept of the algorithm is to gradually increase by one the number of the Radial Basis Function (RBF) units at each learning step thus gradually improving the total model quality. The algorithm stops automatically, when a predetermined (desired) approximation error is achieved. An essential part in the whole algorithm plays the optimization procedure that is run at each step for increasing the number of the RBFs, but only for the parameters of the newly added RBF unit. The parameters of all previously RBFs are kept at their optimized values at the previous learning steps. Such optimization strategy, while suboptimal in nature, leads to significant reduction in the number of the parameters that have to be optimized at each learning step. A modified constraint version of the particle swarm optimization (PSO) algorithm with inertia weight is developed and used in the paper. It allows obtaining conditional optimum solutions within the range of preliminary given boundaries, which have real practical meaning. A synthetic nonlinear test example is used in the paper to estimate the performance of the proposed growing algorithm for nonlinear approximation. Another application of the Growing RBFN model is illustrated on two examples for data classification, one of them from the publicly available red wine quality data set.
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