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Output value-based initialization for radial basis function neural networks

✍ Scribed by Alberto Guillén; Ignacio Rojas; Jesús González; Héctor Pomares; L. J. Herrera; O. Valenzuela; F. Rojas


Publisher
Springer US
Year
2007
Tongue
English
Weight
570 KB
Volume
25
Category
Article
ISSN
1370-4621

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This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overal