Linear-wavelet networks

Roberto K. H. Galvão, Victor Becerra, João M. F. Calado, Pedro M. Silva

    Research output: Contribution to journalArticlepeer-review


    This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.
    Original languageEnglish
    Pages (from-to)221-232
    Number of pages12
    JournalInternational Journal of Applied Mathematics and Computer Science
    Issue number2
    Publication statusPublished - 2004


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