Recursive prediction error identification using the nonlinear Wiener model
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Identification of systems containing linear dynamic and static nonlinear elements
Automatica (Journal of IFAC)
Nonparametric identification of Wiener systems
IEEE Transactions on Information Theory
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
Computers & Mathematics with Applications
Technical Communique: Generation of enhanced initial estimates for Hammerstein Systems
Automatica (Journal of IFAC)
Hammerstein-Wiener system estimator initialization
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Estimation of a single-input single-output block-oriented model is studied. The model consists of a linear block embedded between two static nonlinear gains. Hence, it is called N-L-N Hammerstein-Wiener model. First, the model structure is motivated and the disturbance model is discussed. The paper then concentrates on parameter estimation. A relaxation iteration scheme is proposed by making use of a model structure in which the error is bilinear-in-parameters. This leads to a simple algorithm which minimizes the original loss function. The convergence and consistency of the algorithm are studied. In order to reduce the variance error, the obtained linear model is further reduced using frequency weighted model reduction. Simulation study will be used to illustrate the method.