Consistent identification of dynamic networks subject to white noise using Weighted Null-Space Fitting

S.J.M. Fonken (Corresponding author), Mina Ferizbegovic (Corresponding author), Håkan Hjalmarsson (Corresponding author)

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)
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Abstract

Identification of dynamic networks has been a flourishing area in recent years. However, there are few contributions addressing the problem of simultaneously identifying all modules in a network of given structure. In principle the prediction error method can handle such problems but this methods suffers from well known issues with local minima and how to find initial parameter values. Weighted Null-Space Fitting is a multi-step least-squares method and in this contribution we extend this method to rational linear dynamic networks of arbitrary topology with modules subject to white noise disturbances. We show that WNSF reaches the performance of PEM initialized at the true parameter values for a fairly complex network, suggesting consistency and asymptotic efficiency of the proposed method.
Original languageEnglish
Pages (from-to)46-51
Number of pages6
JournalIFAC-PapersOnLine
Volume53
Issue number2
DOIs
Publication statusPublished - 2020

Funding

★This work was supported by the research environment NewLEADS—New Directions in Learning Dynamical Systems, cont★raTcht is201w6-o0r6k079w;asandsuWppaollretnebdergbyAI,thAeutorensoemarocuhs Seynsvtiermonsmaenndt NoefwtwLaErAe DPSro—grNaemw(DWirAeSctPio),nsfuinndLeedarbnyinKgnDutynaanmdicAallicSeysWteamllesn, bcoenrg-troaucntd2a0ti1o6n-.06079; and Wallenberg AI, Autonomous Systems and SohfitswaprreojPecrotgrhaams r(eWceAivSePd),ffuunnddinedg bfyroKmnuthteanEduArolpiceeanWaRlleesnebarecrhg Coouunndcailti(oEnR. C), Advanced Research Grant SYSDYNET, under the TuhrisopperaonjeUctniohnass Hroecrieziovned20f2u0ndriensgearfcrhomandthiennEouvarotipoenanproRgersaemarmche CGoruannctilA(gErReeCm),enAtdNvaon. c6e9d45R0e4s)e.arch Grant SYSDYNET, under the European Unions Horizon 2020 research and innovation programme (Grant Agreement No. 694504). This work was supported by the research environment NewLEADS-New Directions in Learning Dynamical Systems, contract 2016-06079; and Wallenberg AI, Autonomous Systems and Software Program (WASP), funded by Knut and Alice Wallenberg Foundation. This project has received funding from the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Unions Horizon 2020 research and innovation programme (Grant Agreement No. 694504).

FundersFunder number
Autonomous Systems and SohfitswaprreojPecrotgrhaams r
Horizon 2020 Framework Programme
European Research Council
Knut och Alice Wallenbergs Stiftelse
Horizon 2020694504

    Keywords

    • system identification
    • Dynamic networks
    • Least-squares
    • System identification

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