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Identification of the Silverbox Benchmark Using Nonlinear State-Space Models

Anna Marconato ; Jonas Sjöberg (Institutionen för signaler och system, Mekatronik) ; Johan Suykens ; Johan Schoukens
IFAC Proceedings. 16th IFAC Symposium on System Identification (1474-6670). Vol. 16 (2012), 1, p. 632-637.
[Konferensbidrag, refereegranskat]

This work presents the application of an initialization scheme for nonlinear state-space models on a real data benchmark example: the Silverbox problem. The goal of the proposed approach is to transform the identification of a nonlinear dynamic system into an approximate static problem, so that system dynamics and nonlinear terms are identified separately. Classic identification techniques are used to handle dynamics, while regression methods from the statistical learning community are introduced to estimate the nonlinearities in the model. Results obtained on the Silverbox problem are discussed and compared with the performance of other related methods.

Nyckelord: Nonlinear System Identification; Neural Networks



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Denna post skapades 2012-09-14. Senast ändrad 2015-02-09.
CPL Pubid: 163372

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Mekatronik

Ämnesområden

Energi
Reglerteknik
Signalbehandling

Chalmers infrastruktur