Abstract
In this paper the QR-cp factorization and Singular Value Decomposition (SVD) matrix numerical procedures are used for the optimization of the structure of Radial Basis Function (RBF) neural networks—that is, the best number of input nodes and also the number of neurons within the network. We study the application domain of time series prediction and demonstrate the superior performance of our method for on-line prediction of a well known chaotic time series. A new strategy that consists of the initial allocation of successive groups of nodes is also suggested, since it leads to initially faster learning.
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© 1999 Springer-Verlag Berlin Heidelberg
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Salmerón, M., Ortega, J., Puntonet, C.G. (1999). On-line optimization of radial basis function networks with orthogonal techniques. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098204
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DOI: https://doi.org/10.1007/BFb0098204
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