Linear Switching System Identification Applied to Blast Furnace Data

Amir H. Shirdel, Kaj-Mikael Björk, Markus Holopainen, Christer Carlsson, Hannu T. Toivonen

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

1 Citeringar (Scopus)

Sammanfattning

Switching systems are dynamical systems which can switch between a number of modes characterized by different dynamical behaviors. Several approaches have recently been presented for experimental identification of switching system, whereas studies on real-world applications have been scarce. This paper is focused on applying switching system identification to a blast furnace process. Specifically, the possibility of replacing nonlinear complex system models with a number of simple linear models is investigated. Identification of switching systems consists of identifying both the individual dynamical behavior of model which describes the system in the various modes, as well as the time instants when the mode changes have occurred. In this contribution a switching system identification method based on sparse optimization is used to construct linear switching dynamic models to describe the nonlinear system. The results obtained for blast furnace data are compared with a nonlinear model u sing Artificial Neural Fuzzy Inference System (ANFIS).
OriginalspråkEngelska
Titel på värdpublikationProceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, 2014, Vienna, Austria
Antal sidor6
Volym1
FörlagSCITEPRESS Science And Technology Publications
Utgivningsdatum2014
Sidor643-648
ISBN (tryckt)978-989-758-039-0
DOI
StatusPublicerad - 2014
MoE-publikationstypA4 Artikel i en konferenspublikation

Nyckelord

  • 512 Företagsekonomi

Fingeravtryck

Fördjupa i forskningsämnen för ”Linear Switching System Identification Applied to Blast Furnace Data”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här