An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
The Hammerstein–Wiener Model for Identification of Stochastic Systems
Automation and Remote Control
Adaptive Digital Control of Hammerstein Nonlinear Systems with Limited Output Sampling
SIAM Journal on Control and Optimization
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Brief Estimation of an N-L-N Hammerstein-Wiener model
Automatica (Journal of IFAC)
Decoupling the linear and nonlinear parts in Hammerstein model identification
Automatica (Journal of IFAC)
Identification for multirate multi-input systems using the multi-innovation identification theory
Computers & Mathematics with Applications
The residual based interactive least squares algorithms and simulation studies
Computers & Mathematics with Applications
Brief paper: Least squares based iterative identification for a class of multirate systems
Automatica (Journal of IFAC)
Gradient based and least-squares based iterative identification methods for OE and OEMA systems
Digital Signal Processing
Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems
Computers & Mathematics with Applications
Transformations between some special matrices
Computers & Mathematics with Applications
Auxiliary model-based RELS and MI-ELS algorithm for Hammerstein OEMA systems
Computers & Mathematics with Applications
Iterative solutions to matrix equations of the form AiXBi=Fi
Computers & Mathematics with Applications
Input--output data filtering based recursive least squares identification for CARARMA systems
Digital Signal Processing
Several multi-innovation identification methods
Digital Signal Processing
Gradient-based iterative parameter estimation for Box-Jenkins systems
Computers & Mathematics with Applications
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Computers & Mathematics with Applications
Identification methods for Hammerstein nonlinear systems
Digital Signal Processing
Brief paper: Making parametric Hammerstein system identification a linear problem
Automatica (Journal of IFAC)
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Observable state space realizations for multivariable systems
Computers & Mathematics with Applications
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems
Mathematical and Computer Modelling: An International Journal
Identification for the second-order systems based on the step response
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Time series AR modeling with missing observations based on the polynomial transformation
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Parameter estimation for nonlinear dynamical adjustment models
Mathematical and Computer Modelling: An International Journal
Information Sciences: an International Journal
Hi-index | 0.09 |
An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein-Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. Two methods of separating the parameter estimates of the original parameters from the product terms are discussed: the average method and the singular value decomposition method. To improve the identification accuracy, an extended stochastic gradient algorithm with a forgetting factor is presented. The simulation results indicate that the parameter estimation errors become small by introducing the forgetting factor.