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Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem
KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. (System identification)ORCID-id: 0000-0001-5474-7060
KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik.ORCID-id: 0000-0002-9368-3079
2018 (engelsk)Inngår i: 18th IFAC Symposium on System Identification, 2018Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presentedand discussed.

sted, utgiver, år, opplag, sider
2018.
Serie
IFAC-PapersOnLine
Emneord [en]
Nonlinear system identication, Stochastic systems, Wiener-Hammerstein, Benchmark problem.
HSV kategori
Forskningsprogram
Elektro- och systemteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-233635OAI: oai:DiVA.org:kth-233635DiVA, id: diva2:1242282
Konferanse
18th IFAC Symposium on System Identification, July 9-11, 2018. Stockholm, Sweden
Forskningsfinansiär
Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079EU, European Research Council, 267381
Merknad

QC 20180828

Tilgjengelig fra: 2018-08-27 Laget: 2018-08-27 Sist oppdatert: 2018-08-29bibliografisk kontrollert

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