On the error sensitivity measure for pruning RBF networks

Error sensitivity measure is normally a commonly used factor for searching the optimal structure of a neural network. Starting with the derivation of a recursive equation for the update of a reduced order parametric vector based on the full order parametric vector, the error sensitivity measure for use in linear regressor and RBF network pruning is re-derived and an approximated error sensitivity measure identical to that of proposed in optimal brain damage has been obtained. Considering the training is accomplished by recursive least square method, an on-line training-pruning algorithm is proposed.