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
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
using Artificial Neural Fuzzy Inference System (ANFIS).
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