Title :
Nonlinear PLS modelling using radial basis functions
Author :
Wilson, D.J.H. ; Irwin, G.W. ; Lightbody, G.
Author_Institution :
Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK
Abstract :
An approach to nonlinear partial least squares (PLS) modelling using radial basis function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a technique (the hybrid BFGS algorithm) for training the networks. Results are given to show the performance with a number of different simulation examples, including a model of an industrial overheads condenser and reflux drum plant. Results confirm a significant improvement over linear PLS
Keywords :
feedforward neural nets; least squares approximations; process control; RBF neural networks; industrial overheads condenser; nonlinear inner relationship; nonlinear partial least squares modelling; radial basis function neural networks; reflux drum plant; Clustering algorithms; Control engineering; Cost function; Electrical equipment industry; Electronic mail; Industrial training; Least squares methods; Neural networks; Radial basis function networks; Spline;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.612069
