<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the classical kernel nonparametric estimate, regardless of the number of measurements. The convergence in probability of the proposed estimate to the unknown characteristic is proved and the question of the convergence rate is discussed. Illustrative simulation examples are included.</p></abstract>

A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the classical kernel nonparametric estimate, regardless of the number of measurements. The convergence in probability of the proposed estimate to the unknown characteristic is proved and the question of the convergence rate is discussed. Illustrative simulation examples are included.

Generalized Kernel Regression Estimate for the Identification of Hammerstein Systems

Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Janiszewskiego 11/17, 50-372 Wrocław, Poland

International Journal of Applied Mathematics and Computer Science

A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of...

Generalized Kernel Regression Estimate for the Identification of Hammerstein Systems

Online veröffentlicht: 17. Juli 2007

Seitenbereich: 189 - 197

DOI: https://doi.org/10.2478/v10006-007-0018-z

Schlüsselwörter
Keywords: Hammerstein system, nonparametric regression, kernel estimation

This content is open access.

Generalized Kernel Regression Estimate for the Identification of Hammerstein Systems

Online veröffentlicht: 17. Juli 2007

Seitenbereich: 189 - 197

DOI: https://doi.org/10.2478/v10006-007-0018-z

SchlüsselwörterKeywords: Hammerstein system, nonparametric regression, kernel estimation

This content is open access.

Schlüsselwörter
Keywords: Hammerstein system, nonparametric regression, kernel estimation