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.
|Title of host publication||15th Triennial World Congress of the International Federation of Automatic Control (IFAC)|
|Publisher||International Federation of Automatic Control (IFAC)|
|Publication status||Published - 2002|
- neural-network models, system identification, nonlinear models, function approximation, non-parametric identification, fermentation processes