Fast and good initialization of RBF networks

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 de ned and new nodes to the RBF network are introduced one at the time. The de nition of the grid points is done in a speci c 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.