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Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Reglerteknik.ORCID-id: 0000-0001-5474-7060
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Reglerteknik.ORCID-id: 0000-0002-9368-3079
2018 (engelsk)Inngår i: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, nr 15, s. 784-789Artikkel i tidsskrift (Fagfellevurdert) Published
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 presented and discussed.

sted, utgiver, år, opplag, sider
Elsevier B.V. , 2018. Vol. 51, nr 15, s. 784-789
Emneord [en]
Benchmark problem, Nonlinear systems, Stochastic systems, System identification, Wiener-Hammerstein, Error analysis, Identification (control systems), Monte Carlo methods, Stochastic models, Bench-mark problems, Benchmark data, Estimation problem, Likelihood functions, Monte-carlo approximations, Prediction error method, Wiener-hammerstein models, Benchmarking
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Identifikatorer
URN: urn:nbn:se:kth:diva-247494DOI: 10.1016/j.ifacol.2018.09.135ISI: 000446599200133Scopus ID: 2-s2.0-85054433381OAI: oai:DiVA.org:kth-247494DiVA, id: diva2:1301855
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QC 20190403

Tilgjengelig fra: 2019-04-03 Laget: 2019-04-03 Sist oppdatert: 2022-09-15bibliografisk kontrollert

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Abdalmoaty, Mohamed R.Hjalmarsson, Håkan

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