Linear-wavelet models for system identification

R. K. H. Galvão, V. M. Becerra

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    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.
    Original languageEnglish
    Title of host publication15th Triennial World Congress of the International Federation of Automatic Control (IFAC)
    PublisherInternational Federation of Automatic Control (IFAC)
    Publication statusPublished - 2002


    • neural-network models, system identification, nonlinear models, function approximation, non-parametric identification, fermentation processes


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