A sequential least squares algorithm for ARMAX dynamic network identificationVisa övriga samt affilieringar
2018 (Engelska)Ingår i: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, nr 15, s. 844-849Artikel i tidskrift (Refereegranskat) Published
Abstract [en]
Identification of dynamic networks in prediction error setting often requires the solution of a non-convex optimization problem, which can be difficult to solve especially for large-scale systems. Focusing on ARMAX models of dynamic networks, we instead employ a method based on a sequence of least-squares steps. For single-input single-output models, we show that the method is equivalent to the recently developed Weighted Null Space Fitting, and, drawing from the analysis of that method, we conjecture that the proposed method is both consistent as well as asymptotically efficient under suitable assumptions. Simulations indicate that the sequential least squares estimates can be of high quality even for short data sets.
Ort, förlag, år, upplaga, sidor
Elsevier B.V. , 2018. Vol. 51, nr 15, s. 844-849
Nyckelord [en]
dynamic networks, identification algorithm, least squares, System identification, Convex optimization, Identification (control systems), Large scale systems, Asymptotically efficient, Dynamic network, Identification algorithms, Least Square, Least squares algorithm, Least squares estimate, Nonconvex optimization, Single input single output, Least squares approximations
Nationell ämneskategori
Reglerteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-247491DOI: 10.1016/j.ifacol.2018.09.119ISI: 000446599200143Scopus ID: 2-s2.0-85054462289OAI: oai:DiVA.org:kth-247491DiVA, id: diva2:1304504
Anmärkning
QC20190412
2019-04-122019-04-122022-09-15Bibliografiskt granskad