Abstract
This paper deals with the application of the Support Vector Method (SVM) methodology to the Auto Regressive and Moving Average (ARMA) linear-system identification problem. The SVM-ARMA algorithm for a single-input single-output transfer function is formulated. The relationship between the SVM coefficients and the residuals, together with the embedded estimation of the autocorrelation function, are presented. Also, the effect of the numerical regularization is used to highlight the robust cost character of this approach. A clinical example is presented for qualitative comparison with the classical Least Squares (LS) methods.
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© 2002 Springer-Verlag Berlin Heidelberg
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Rojo-Álvarez, J.L., Martínez-Ramón, M., Figueiras-Vidal, A.R., de Prado-Cumplido, M., Artés-Rodríguez, A. (2002). Support Vector Method for ARMA System Identification: A Robust Cost Interpretation. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_179
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DOI: https://doi.org/10.1007/3-540-46084-5_179
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