Abstract
In this paper, a new class of learning models, namely the additive radial basis function networks (ARBFNs) for general nonlinear regression problems are proposed. This class of learning machines combines the radial basis function networks (RBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semiparametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the nonparametric model. Simulation results show that for the given learning problem, ARBFNs usually need fewer hidden nodes than those of RBFNs for the same level of accuracy.
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