Name
Abstract | ||
|---|---|---|
This paper discusses a novel initialization algorithm for the estimation of nonlinear state-space models. Good initial values for the model parameters are obtained by identifying separately the linear dynamics and the nonlinear terms in the model. In particular, the nonlinear dynamic problem is transformed into an approximate static formulation, and simple regression methods are applied to obtain the solution in a fast and efficient way. The proposed method is validated by means of two measurement examples: the Wiener-Hammerstein benchmark problem and the identification of a crystal detector. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIM.2013.2283553 | Instrumentation and Measurement, IEEE Transactions |
Keywords | Field | DocType |
modelling,nonlinear dynamical systems,regression analysis,Wiener-Hammerstein benchmark problem,approximate static formulation,crystal detector,linear dynamics,nonlinear dynamic problem,nonlinear state-space modeling,nonlinear terms,novel initialization algorithm,simple regression methods,Multilayer perceptrons,nonlinear dynamical systems,nonlinear modeling,state-space models,system identification | Data modeling,Nonlinear system,Regression analysis,Control theory,Algorithm,Control engineering,Simple linear regression,Initialization,System identification,Dynamic problem,State space,Mathematics | Journal |
Volume | Issue | ISSN |
63 | 4 | 0018-9456 |
Citations | PageRank | References |
8 | 0.55 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna Marconato | 1 | 24 | 4.42 |
| Jonas Sjöberg | 2 | 628 | 67.21 |
| Johan A. K. Suykens | 3 | 635 | 53.51 |
| Johan Schoukens | 4 | 31 | 1.92 |