Nonlinear PLS modelling using radial basis functions

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.