Abstract
One of the main problems associated with artificial neural networks
on-line learning methods is the
estimation of model order.
In this paper, we report about a new approach to constructing a
resource-allocating radial basis function network exploiting
weights adaptation using
recursive least-squares technique based on Givens QR decomposition.
Further, we study
the performance of pruning strategy we introduced to obtain the
same prediction accuracy of the network with lower model order. The
proposed methods were tested on the task of Mackey-Glass
time-series prediction. Order of resulting
networks and their prediction performance were superior to those
previously reported by Platt.
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