14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper ThA1.5

Espinoza, Marcelo (K.U.Leuven), Suykens, Johan (K.U. Leuven), De Moor, Bart (K.U.Leuven)

LS-SVM Regression with Autocorrelated Errors

Scheduled for presentation during the Invited Session "Nonlinear System Identification I" (ThA1), Thursday, March 30, 2006, 12:10−12:30, Concert Hall

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Nonlinear System Identification, Nonparametric Methods, Grey Box Modelling


The problem of nonlinear AR(X) system identification with correlated residuals is addressed. Using LS-SVM regression as a nonlinear black-box technique, it is illustrated that neglecting such correlation can have negative effects on the identification stage. We show that when the correlation structure of the residuals is explicitly incorporated into the model, this information is embedded into the kernel level in the dual space solution. The solution can be obtained from a convex problem, in which the correlation coefficients are considered to be tuning parameters. The dynamical structure of the model is explored in terms of an equivalent NAR(X)-AR representation, for which the optimal one-step-ahead predictor is expressed in terms of the approximated nonlinear function and the correlation structure.