Fast and Good Initialization of RBF Networks
Paper i proceeding, 2010

In this paper a new method for fast initialization of radial basis function (RBF) networks is proposed. A grid of possible positions and widths for the basis functions is defined and new nodes to the RBF network are introduced one at the time. The definition of the grid points is done in a specific way which leads to algorithms which are computationally inexpensive due to the fact that intermediate results can be reused and do not need to be re-computed. If the grid is dense one obtains estimators close to estimators resulting from an exhaustive search for the initial parameters which leads to a lower risk to be caught in local minima in the minimization which follows. The usefulness of the approach is demonstrated in a simulation example.


Dietmar Bauer

Austrian Institute of Technology

Jonas Sjöberg

Signaler och system, System- och reglerteknik, Mekatronik

Proceeding of the 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 28 - 30 April 2010



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