Initialization and model reduction for Wiener model identification
1999 (English)Report (Other academic)
The identification of nonlinear systems by the minimization of a predictionerror criterion suffers from the problem of local minima. To get a reliableestimate we need good initial values for the parameters. In this paper wediscuss the class of nonlinear Wiener models, consisting of a linear dynamicsystem followed by a static nonlinearity. By selecting a parameterizationwhere the parameters enter linearly in the error, we can obtain an initialestimate of the model via linear regression. An example shows that thisapproach may be preferential to trying to estimate the linear system directlyform input-output data, if the input is not Gaussian. We discuss some of theusers choices and how the linear regression initial estimate can be convertedto a desired model structure to use in the prediction error criterionminimization. The method is also applied to experimental data.
Place, publisher, year, edition, pages
1999. , 8 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2150
Wiener models, initialization
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-55719ISRN: LiTH-ISY-R-2150OAI: oai:DiVA.org:liu-55719DiVA: diva2:316476