System identification: theory for the user
System identification: theory for the user
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
Computers & Mathematics with Applications
Brief paper: Convergence of the iterative algorithm for a general Hammerstein system identification
Automatica (Journal of IFAC)
Identification of Hammerstein nonlinear ARMAX systems
Automatica (Journal of IFAC)
Finite model order accuracy in Hammerstein model estimation
Automatica (Journal of IFAC)
Quasiconvexity analysis of the Hammerstein model
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
In this paper, we study the identification of parametric Hammerstein systems with FIR linear parts. By a proper normalization and a clever characterization, it is shown that the average squared error cost function for identification can be expressed in terms of the inner product between the true but unknown parameter vector and its estimate. Further, the cost function is concave in the inner product and linear in the inner product square. Therefore, the identification of parametric Hammerstein systems with FIR linear parts is a globally convergent problem and has one and only one (local and global) minimum. This implies that the identification of such systems is a linear problem in terms of the inner product square and any local search based identification algorithm converges globally.