15th Triennial World Congress of the International Federation of Automatic Control
  Barcelona, 21–26 July 2002 
Roberto K. H. Galvão*, Victor M. Becerra**
* ITA, Div. Eng. Eletrônica, São José dos Campos – SP, 12228–900, Brazil
** University of Reading, Cybernetics Department, Reading RG6 6AY, UK

A model structure comprising a wavelet network and a linear term is proposed for nonlinear system identification. It is shown that under certain conditions wavelets are orthogonal to linear functions and, as a result, the two parts of the model can be identified separately. The linear-wavelet model is compared to a standard wavelet network using data from a simulated fermentation process. The results show that the linear-wavelet model yields a smaller modelling error when compared to a wavelet network using the same number of regressors.
Keywords: Neural-network models, System identification, Nonlinear models, Function approximation, Non-parametric identification, Fermentation processes
Session slot T-Tu-E01: Soft Computing and Wavelets in Identification/Area code 3a : Modelling, Identification and Signal Processing