Exploiting the functional training approach in Radial Basis Function networks

Cabrita, Cristiano LourençoRuano, AntonioFerreira, P. M.2013-01-312013-01-312011Cabrita, Cristiano L.; Ruano, Antonio E; Ferreira, Pedro M. Exploiting the functional training approach in Radial Basis Function networks, Trabalho apresentado em 2011 IEEE 7th International Symposium on Intelligent Signal Processing - (WISP 2011), In Proceedings of the 2011 IEEE 7th International Symposium on Intelligent Signal Processing, Floriana, Malta, 2011.978-1-4577-1403-0AUT: ARU00698; CCA01443;http://hdl.handle.net/10400.1/2165This paper investigates the application of a novel approach for the parameter estimation of a Radial Basis Function (RBF) network model. The new concept (denoted as functional training) minimizes the integral of the analytical error between the process output and the model output [1]. In this paper, the analytical expressions needed to use this approach are introduced, both for the back-propagation and the Levenberg- Marquardt algorithms. The results show that the proposed methodology outperforms the standard methods in terms of function approximation, serving as an excellent tool for RBF networks training.engRadial Basis Neural networks trainingLocal nonlinear optimizationParameter separabilityFunctional backpropagationExploiting the functional training approach in Radial Basis Function networksconference object2013-01-26http://dx.doi.org/10.1109/WISP.2011.6051694