Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2165
Título: Exploiting the functional training approach in Radial Basis Function networks
Autor: Cabrita, Cristiano Lourenço
Ruano, A. E.
Ferreira, P. M.
Palavras-chave: Radial Basis Neural networks training
Local nonlinear optimization
Parameter separability
Functional backpropagation
Data: 2011
Editora: IEEE
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.
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2165
DOI: http://dx.doi.org/10.1109/WISP.2011.6051694
ISBN: 978-1-4577-1403-0
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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