  In this paper an identification method for state-space LPV models is
presented. The method is based on a particular parameterization that can be
written in linear regression form and enables model estimation to be handled
using Least-Squares Support Vector Machine (LS-SVM). The regression form has a
set of design variables that act as filter poles to the underlying basis
functions. In order to preserve the meaning of the Kernel functions (crucial in
the LS-SVM context), these are filtered by a 2D-system with the predictor
dynamics. A data-driven, direct optimization based approach for tuning this
filter is proposed. The method is assessed using a simulated example and the
results obtained are twofold. First, in spite of the difficult nonlinearities
involved, the nonparametric algorithm was able to learn the underlying
dependencies on the scheduling signal. Second, a significant improvement in the
performance of the proposed method is registered, if compared with the one
achieved by placing the predictor poles at the origin of the complex plane,
which is equivalent to considering an estimator based on an LPV auto-regressive
(LPV-ARX) structure.
