Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identication of a Class of Nonlinear Dynamical Networks
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2018 (English)Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
2018.
Series
IFAC-PapersOnLine
Keywords [en]
System Identication, Dynamical Networks, Stochastic Systems, Block-Oriented Models, Prediction Error Method.
National Category
Signal Processing Control Engineering
Research subject
Electrical Engineering; Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-233639OAI: oai:DiVA.org:kth-233639DiVA, id: diva2:1242285
Conference
18th IFAC Symposium on System Identification
Funder
EU, European Research Council, 267381Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079
Note

QC 20180829

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

Open Access in DiVA

0131.pdf(470 kB)115 downloads
File information
File name FULLTEXT01.pdfFile size 470 kBChecksum SHA-512
0eaa3461b6e9df88722c4e71f87cdefa9fa7e79fb055ebaf257af1ed00a26f228490465c4310a557886a907f8c95fc0751c307e703bb78625e0227e95426b290
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Abdalmoaty, Mohamed R.Rojas, Cristian R.Hjalmarsson, Håkan
By organisation
Automatic ControlACCESS Linnaeus Centre
Signal ProcessingControl Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 115 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1517 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf