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Exploiting the functional training approach in Radial Basis Function networks
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Orientador(es)
Resumo(s)
This 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.
Descrição
Palavras-chave
Radial Basis Neural networks training Local nonlinear optimization Parameter separability Functional backpropagation
Contexto Educativo
Citação
Cabrita, 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.
Editora
IEEE