Comparison of radial basis function approximation techniques

Author:

Coulomb Jean‐Louis,Kobetski Avenir,Caldora Costa Mauricio,Mare´chal Yves,Jo¨nsson Ulf

Abstract

This paper compares three different radial basis function neural networks, as well as the diffuse element method, according to their ability of approximation. This is very useful for the optimization of electromagnetic devices. Tests are done on several analytical functions and on the TEAM workshop problem 25.

Publisher

Emerald

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computational Theory and Mathematics,Computer Science Applications

Reference8 articles.

1. Alotto, P., Caiti, A., Molinari, G. and Repetto, M. (1996), “A multiquadrics‐based algorithm for the acceleration of simulated annealing optimization procedures”, IEEE Transactions on Magnetics, Vol. 32 No. 3.

2. Caldora Costa, M., Coulomb, J.L. and Maréchal, Y. (2001a), Parameters screening of Team workshop problem 25 by the application of experimental design method, ISTET 2001: International Symposium on Theoretical Electrical Engineering.

3. Caldora Costa, M., Coulomb, J.L., Maréchal, Y., Dietrich, A.B. and Nabeta, S.I. (2001b), Diffuse element method and quadtrees: two ingredients for an adaptive response surface, COMPUMAG 2001.

4. Flux (2002), Cedrat, 10, Chemin de Pré Carré – ZIRST, 38246 Meylan, France, http://www.cedrat.com

5. Poggio, T. and Girosi, F. (1990), “Networks for approximation and learning”, Proceedings of the IEEE, Vol. 78 No. 9.

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