Nonparametric identification of a cascade nonlinear time series system
Signal Processing
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
A blind approach to Hammerstein model identification
IEEE Transactions on Signal Processing
Technical communique: Initial estimates for the dynamics of a Hammerstein system
Automatica (Journal of IFAC)
Reduced order modelling of linear multivariable systems using particle swarm optimisation technique
International Journal of Innovative Computing and Applications
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Brief paper: Convergence of the iterative algorithm for a general Hammerstein system identification
Automatica (Journal of IFAC)
Nonlinear models of physiological parameters based on exercise spiroergometric tests
CSCS '11 Proceedings of the 2nd international conference on Circuits, systems, control, signals
Expert Systems with Applications: An International Journal
Finite model order accuracy in Hammerstein model estimation
Automatica (Journal of IFAC)
Nonlinear modelling and control for heart rate response to exercise
International Journal of Bioinformatics Research and Applications
Hi-index | 22.15 |
This paper studies a method for the identification of Hammerstein models based on least squares support vector machines (LS-SVMs). The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part. This is done by applying the equivalent of Bai's overparameterization method for identification of Hammerstein systems in an LS-SVM context. The SISO as well as the MIMO identification cases are elaborated. The technique can lead to significant improvements with respect to classical overparameterization methods as illustrated in a number of examples. Another important advantage is that no stringent assumptions on the nature of the nonlinearity need to be imposed except for a certain degree of smoothness.