Abstract

We present a new algorithm for the construction of radial basis function (RBF) networks. The method uses accumulated error information to determine where to insert new units. The diameter of the localized units is chosen based on the mutual distances of the units. To have the distance information always available, it is held up-to-date by a Hebbian learning rule adapted from the "Neural Gas" algorithm. The new method has several advantages over existing methods and is able to generate small, well-generalizing networks with comparably few sweeps through the training data. 1 Introduction We propose a new algorithm for the construction of radial basis function (RBF) networks. An RBF network consists of a number of localized units (we use Gaussians in the following) positioned in input vector space. The localized units are completely connected by a set of weighted connections to a set of output units. The output units simply do a summation over their inputs. Networks of this type and vari...

Citations

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