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Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem
KTH, School of Electrical Engineering (EES), Automatic Control. (System identification)ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-9368-3079
2018 (English)In: 18th IFAC Symposium on System Identification, 2018Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
2018.
Series
IFAC-PapersOnLine
Keywords [en]
Nonlinear system identication, Stochastic systems, Wiener-Hammerstein, Benchmark problem.
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-233635OAI: oai:DiVA.org:kth-233635DiVA, id: diva2:1242282
Conference
18th IFAC Symposium on System Identification, July 9-11, 2018. Stockholm, Sweden
Funder
Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079EU, European Research Council, 267381
Note

QC 20180828

Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-08-29Bibliographically approved

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Abdalmoaty, Mohamed R.Hjalmarsson, Håkan

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CiteExportLink to record
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