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Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-9368-3079
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 784-789Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier B.V. , 2018. Vol. 51, no 15, p. 784-789
Keywords [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
National Category
Control Engineering
Identifiers
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
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-05-20Bibliographically approved

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Abdalmoaty, Mohamed R.Hjalmarsson, Håkan

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