Online Kernel Canonical Correlation Analysis for Supervised Equalization of Wiener Systems

Steven Van Vaerenbergh, Member, IEEE, Javier Vía, Member, IEEE, and Ignacio Santamaría, Member, IEEE
Dept. of Communications Engineering, University of Cantabria, Spain
E-mail: {steven,jvia,nacho}@gtas.dicom.unican.es

Abstract:

We consider the application of kernel canonical correlation analysis (K-CCA) to the supervised equalization of Wiener systems. Although a considerable amount of research has been carried out on identification/equalization of Wiener models, in this paper we show that K-CCA is a particularly suitable technique for the inversion of these nonlinear dynamic systems. Another contribution of this paper is the development of an online K-CCA algorithm which combines a sliding-window approach with a recently proposed reformulation of CCA as an iterative regression problem. This online algorithm permits fast equalization of time-varying Wiener systems. Simulation examples are added to illustrate the performance of the proposed method.