ISSN:
1573-773X
Keywords:
RBF neural networks
;
neural networks design
;
statistical analysis of RBF
;
RBF structures
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract The main architectures, learning abilities and applications of radial basis function (RBF) neural networks are well documented. However, to the best of our knowledge, no in-depth analyses have been carried out into the influence on the behaviour of the neural network arising from the use of different alternatives for the design of an RBF (different non-linear functions, distances, number of neurons, structures, etc.). Thus, as a complement to the existing intuitive knowledge, it is necessary to have a more precise understanding of the significance of the different alternatives. In the present contribution, the relevance and relative importance of the parameters involved in such a design are investigated by using a statistical tool, the ANalysis Of the VAriance (ANOVA). In order to obtain results that are widely applicable, various problems of classification, functional approximation and time series estimation are analyzed. Conclusions are drawn regarding the whole set.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1009621931185
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