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Identication of a Class of Nonlinear Dynamical Networks
KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.ORCID-id: 0000-0001-5474-7060
KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.ORCID-id: 0000-0003-0355-2663
KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre.ORCID-id: 0000-0002-9368-3079
2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Identifcation of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

sted, utgiver, år, opplag, sider
2018.
Serie
IFAC-PapersOnLine
Emneord [en]
System Identication, Dynamical Networks, Stochastic Systems, Block-Oriented Models, Prediction Error Method.
HSV kategori
Forskningsprogram
Elektro- och systemteknik; Elektro- och systemteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-233639OAI: oai:DiVA.org:kth-233639DiVA, id: diva2:1242285
Konferanse
18th IFAC Symposium on System Identification
Forskningsfinansiär
EU, European Research Council, 267381Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079
Merknad

QC 20180829

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

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