15 / 2017-12-27 21:24:54

A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation

Growing, neuron’s significance, pruning, radial basis networks, sequential learning.

This paper presents a new sequential learning algorithm for radial basis function (RBF) networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF).
The paper first introduces the concept of significance for the
hidden neurons and then uses it in the learning algorithm to
realize parsimonious networks. The growing and pruning strategy
of GGAP-RBF is based on linking the required learning accuracy
with the significance of the nearest or intentionally added new
neuron. Significance of a neuron is a measure of the average information content of that neuron. The GGAP-RBF algorithm can be
used for any arbitrary sampling density for training samples and
is derived from a rigorous statistical point of view. Simulation results for bench mark problems in the function approximation area
show that the GGAP-RBF outperforms several other sequential
learning algorithms in terms of learning speed, network size and
generalization performance regardless of the sampling density
function of the training data.