Exploiting the functional training approach in Radial Basis Function networks

Cabrita, Cristiano Lourenço; Ruano, Antonio; Ferreira, P. M.

http://hdl.handle.net/10400.1/2165

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Authors

Cabrita, Cristiano Lourenço

Ruano, Antonio

Ferreira, P. M.

Abstract(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.

Keywords

Radial Basis Neural networks training Local nonlinear optimization Parameter separability Functional backpropagation

URI

http://hdl.handle.net/10400.1/2165

Citation

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.