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Authors: Amir H. Shirdel 1 ; Kaj-Mikael Björk 2 ; Markus Holopainen 1 ; Christer Carlsson 1 and Hannu T. Toivonen 1

Affiliations: 1 Åbo Akademi University, Finland ; 2 Arcada University of Applied Sciences and Åbo Akademi University, Finland

ISBN: 978-989-758-039-0

Keyword(s): System Identification, Linear Switching System, Blast Furnace, ANFIS, Nonlinear System, Sparse Optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Enterprise Information Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Modeling, Analysis and Control of Hybrid Dynamical Systems ; Optimization Algorithms ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; System Identification ; System Modeling

Abstract: 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). (More)

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Paper citation in several formats:
H. Shirdel, A.; Björk, K.; Holopainen, M.; Carlsson, C. and T. Toivonen, H. (2014). Linear Switching System Identification Applied to Blast Furnace Data.In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-039-0, pages 643-648. DOI: 10.5220/0005022806430648

@conference{icinco14,
author={Amir H. Shirdel. and Kaj{-}Mikael Björk. and Markus Holopainen. and Christer Carlsson. and Hannu T. Toivonen.},
title={Linear Switching System Identification Applied to Blast Furnace Data},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={643-648},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005022806430648},
isbn={978-989-758-039-0},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Linear Switching System Identification Applied to Blast Furnace Data
SN - 978-989-758-039-0
AU - H. Shirdel, A.
AU - Björk, K.
AU - Holopainen, M.
AU - Carlsson, C.
AU - T. Toivonen, H.
PY - 2014
SP - 643
EP - 648
DO - 10.5220/0005022806430648

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