Direct Continuous Time System Identification of MISO Transfer Function Models applied to Type 1 Diabetes

Harald Kirchsteiger; Stephan Pölzer; Rolf Johansson; Eric Renard; Luigi del Re

Direct Continuous Time System Identification of MISO Transfer Function Models applied to Type 1 Diabetes

Harald Kirchsteiger, Stephan Pölzer, Rolf Johansson, Eric Renard, Luigi del Re

Institute of Design and Control of Mechatronical Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

This paper shows an application of continuous time system identification methods to Type 1 diabetes. First, a general MISO transfer function structure with individual nominator and denominator polynomials for each input is assumed and a parameter estimation procedure via an iterative prediction error method presented. Then, the proposed identification method is evaluated on a simple simulation example and finally applied on real-life data from Type 1 diabetes patients with the purpose of modeling blood glucose dynamics. To this aim, the method was extended to consider the time-varying nature of the system.

Original language	English
Title of host publication	Proceedings of the 50th IEEE Conference on Decision and Control
Number of pages	6
Publication status	Published - Dec 2011

Fields of science

203 Mechanical Engineering
202034 Control engineering
202012 Electrical measurement technology
206 Medical Engineering
202027 Mechatronics
202003 Automation
203027 Internal combustion engines
207109 Pollutant emission

JKU Focus areas

Mechatronics and Information Processing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Direct Continuous Time System Identification of MISO Transfer Function Models applied to Type 1 Diabetes",

abstract = "This paper shows an application of continuous time system identification methods to Type 1 diabetes. First, a general MISO transfer function structure with individual nominator and denominator polynomials for each input is assumed and a parameter estimation procedure via an iterative prediction error method presented. Then, the proposed identification method is evaluated on a simple simulation example and finally applied on real-life data from Type 1 diabetes patients with the purpose of modeling blood glucose dynamics. To this aim, the method was extended to consider the time-varying nature of the system.",

author = "Harald Kirchsteiger and Stephan P{\"o}lzer and Rolf Johansson and Eric Renard and {del Re}, Luigi",

year = "2011",

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booktitle = "Proceedings of the 50th IEEE Conference on Decision and Control",

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AU - Kirchsteiger, Harald

AU - Pölzer, Stephan

AU - Johansson, Rolf

AU - Renard, Eric

AU - del Re, Luigi

PY - 2011/12

Y1 - 2011/12

N2 - This paper shows an application of continuous time system identification methods to Type 1 diabetes. First, a general MISO transfer function structure with individual nominator and denominator polynomials for each input is assumed and a parameter estimation procedure via an iterative prediction error method presented. Then, the proposed identification method is evaluated on a simple simulation example and finally applied on real-life data from Type 1 diabetes patients with the purpose of modeling blood glucose dynamics. To this aim, the method was extended to consider the time-varying nature of the system.

AB - This paper shows an application of continuous time system identification methods to Type 1 diabetes. First, a general MISO transfer function structure with individual nominator and denominator polynomials for each input is assumed and a parameter estimation procedure via an iterative prediction error method presented. Then, the proposed identification method is evaluated on a simple simulation example and finally applied on real-life data from Type 1 diabetes patients with the purpose of modeling blood glucose dynamics. To this aim, the method was extended to consider the time-varying nature of the system.

M3 - Conference proceedings

BT - Proceedings of the 50th IEEE Conference on Decision and Control

ER -