Comparison of RBF and local linear model networks for nonlinear identification of a pH process

This paper focuses on the nonlinear identification of an experimental pH neutralisation process using real data. The performances of radial basis function (RBF) and local linear model networks (LLMN) for identifying this significantly nonlinear process are compared. Results are presented to illustrate the choice of the various network parameters in the model structures for network training and validation data. The overall results demonstrate the practical ability of the two network structures for nonlinear system identification.