Maximum certainty approach to feedforward neural networks
A Bayesian-based methodology is presented which leads to a data analysis system based around a committee of radial-basis function (RBF) networks. The authors show that this approach enables estimation of the uncertainty associated with system outputs. Systems with differing numbers of internal degrees of freedom (weights) may hence be compared using training data only.