Identification of Hammerstein-Wiener models

Authors:
Adrian Wills;Thomas B. SchöN;Lennart Ljung;Brett Ninness
Affiliations:
School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia;Division of Automatic Control, Linköping University, SE-581 83 Linköping, Sweden;Division of Automatic Control, Linköping University, SE-581 83 Linköping, Sweden;School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia
Venue:
Automatica (Journal of IFAC)
Year:
2013

Citing 15
Cited 0

Recursive prediction error identification using the nonlinear Wiener model

Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Identifying MIMO Wiener systems using subspace model identification methods

Signal Processing - Special issue: subspace methods, part II: system identification
Maximum likelihood identification of Wiener models

Automatica (Journal of IFAC)
Blind maximum-likelihood identification of wiener systems

IEEE Transactions on Signal Processing
System identification of nonlinear state-space models

Automatica (Journal of IFAC)
Guest editorial special issue on monte carlo methods for statistical signal processing

IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
A Basic Convergence Result for Particle Filtering

IEEE Transactions on Signal Processing
Identification of systems containing linear dynamic and static nonlinear elements

Automatica (Journal of IFAC)
Brief Identification of linear systems with hard input nonlinearities of known structure

Automatica (Journal of IFAC)
A blind approach to the Hammerstein-Wiener model identification

Automatica (Journal of IFAC)
Robust maximum-likelihood estimation of multivariable dynamic systems

Automatica (Journal of IFAC)
Brief Fast approximate identification of nonlinear systems

Automatica (Journal of IFAC)
Estimation of 1/f noise

IEEE Transactions on Information Theory
Hinging hyperplanes for regression, classification, and function approximation

IEEE Transactions on Information Theory

Quantified Score

Hi-index	22.14

Visualization

Abstract

This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case.