A new kernel-based approach to overparameterized Hammerstein system identification
2015 (English)In: 2015 54th IEEE Conference on Decision and Control (CDC), IEEE conference proceedings, 2015, 115-120 p.Conference paper (Refereed)
- Download Citation
- Request Permissions
The object of this paper is the identification of Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of p basis functions. We model the system dynamics by means of an np-dimensional vector. This vector, usually referred to as overparameterized vector, contains all the combinations between the nonlinearity coefficients and the first n samples of the impulse response of the linear block. The estimation of the overparameterized vector is performed with a new regularized kernel-based approach. To this end, we introduce a novel kernel tailored for overparameterized models, which yields estimates that can be uniquely decomposed as the combination of an impulse response and p coefficients of the static nonlinearity. As part of the work, we establish a clear connection between the proposed identification scheme and our recently developed nonparametric method based on the stable spline kernel.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 115-120 p.
IdentifiersURN: urn:nbn:se:kth:diva-186520DOI: 10.1109/CDC.2015.7402095ISBN: 978-1-4799-7884-7OAI: oai:DiVA.org:kth-186520DiVA: diva2:927534
2015 54th IEEE Conference on Decision and Control (CDC),15-18 Dec. 2015, Osaka, Japan
QC 201605202016-05-122016-05-122016-05-20Bibliographically approved