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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A new approach to the non-parametric spectral estimation on the basis of the Support Vector (SV) framework is presented. Two algorithms are derived for both uniform and non-uniform sampling. The relationship between the SV free parameters and the underlying process statistics is discussed. The application in two real data examples, the sunspot numbers and the Heart Rate Variability, shows the higher resolution and robustness in SV spectral analysis algorithms.

Adaptive blind equalization plays an essential role in modern communication systems. We propose to improve the performance of constant modulus algorithm (CMA) based equalizers by using an adaptive convex combination of two CMA filters with a large and small step size, respectively, in order to simultaneously obtain fast convergence with low misadjustment during stationary periods. Some experiments show the effectiveness of the new algorithm and suggest that it is a reasonable alternative to blind equalizers that commute between the CMA and the (decision-directed) least mean square filter.