Title :
Regularised OLS algorithm with fast implementation for training multi-output radial basis function networks
Author_Institution :
Portsmouth Univ., UK
Abstract :
The paper presents an approach for training multi-output radial basis function (RBF) networks by combining subset selection with regularisation. A regularised orthogonal least squares (ROLS) algorithm is derived, which is capable of constructing parsimonious networks that generalise well. A fast implementation of the ROLS algorithm further reduces computational requirements significantly. System identification is used as an example to demonstrate the effectiveness of this training algorithm
Keywords :
feedforward neural nets; generalisation (artificial intelligence); identification; learning (artificial intelligence); least squares approximations; ROLS algorithm; computational requirements; generalisation; multi-output radial basis function networks; neural network training; parsimonious networks; regularisation; regularised OLS algorithm; regularised orthogonal least squares algorithm; subset selection; system identification;
Conference_Titel :
Artificial Neural Networks, 1995., Fourth International Conference on
Conference_Location :
Cambridge
Print_ISBN :
0-85296-641-5
DOI :
10.1049/cp:19950570
