Title :
PLS algorithm for radial basis function networks
Author :
Wang, Yin ; Rong, Gang ; Wang, Shuqing
Author_Institution :
Nat. Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
Abstract :
The PLS (partial least squares) algorithm is introduced into the radial basis function (RBF) networks to construct MIMO nonlinear models. The PLS algorithm projects the correlated basis functions and the outputs down to a number of principal factors to construct parsimonious models. Example of nonlinear modeling is used to demonstrate better generalization and noise tolerable performance of the proposed algorithm than the full RBF network models. Finally, it is used as a product quality predictor for industrial distillation columns
Keywords :
MIMO systems; correlation methods; least squares approximations; modelling; nonlinear systems; radial basis function networks; MIMO nonlinear models; PLS algorithm; RBF networks; correlated basis functions; industrial distillation columns; nonlinear modeling; parsimonious model construction; partial least squares algorithm; product quality predictor; radial basis function networks; Distillation equipment; Industrial control; Laboratories; Least squares methods; Linear regression; MIMO; Predictive models; Radial basis function networks; Training data; Vectors;
Conference_Titel :
Decision and Control, 1998. Proceedings of the 37th IEEE Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
0-7803-4394-8
DOI :
10.1109/CDC.1998.762085
