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 semi parametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the non parametric 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.
|Name||IEEE International Conference on Systems Man and Cybernetics Conference Proceedings|
|Conference||2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010|
|Period||10/10/10 → 13/10/10|
- additive radial basis function network (ARBFN); radial basis function network (RBFN); additive model (AM); semi parametric regression