Rates of convergence of the recursive radial basis function networks

Recursive radial basis function (RRBF) neural networks are introduced and discussed. We study in detail the nets with diagonal receptive field matrices. Parameters of the networks are learned by a simple procedure. Convergence and the rates of convergence of RRBF nets in the mean integrated absolute error (MIAE) sense are studied under mild conditions imposed on some of the network parameters. The obtained results also give the upper bounds on the performance of RRBF nets learned by minimizing the empirical L/sub 1/ error.

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