Adaptive Kernel Canonical Correlation Analysis Algorithms for Nonparametric Identification of Wiener and Hammerstein Systems

EURASIP Journal on Advances in Signal Processing. 2008;2008 DOI 10.1155/2008/875351

 

Journal Homepage

Journal Title: EURASIP Journal on Advances in Signal Processing

ISSN: 1687-6172 (Print); 1687-6180 (Online)

Publisher: Springer

Society/Institution: European Association for Signal Processing (EURASIP)

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication | Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Ignacio Santamaría
Javier Vía
Steven Van Vaerenbergh

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 13 weeks

 

Abstract | Full Text

This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity, such as the well-known Wiener and Hammerstein systems. In particular, we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem, we show how kernel canonical correlation analysis (KCCA) emerges as the logical solution to this problem. We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems, we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm.