DocumentCode
173901
Title
Comparison of sigma-point filters for state estimation of diabetes models
Author
Szalay, Peter ; Molnar, Adrienn ; Muller, Mathias ; Eigner, Gyorgy ; Rudas, Imre ; Benyo, Zoltan ; Kovacs, Levente
Author_Institution
Dept. of Control Eng. & Inf. Technol., Budapest Univ. of Technol. & Econ., Budapest, Hungary
fYear
2014
fDate
5-8 Oct. 2014
Firstpage
2476
Lastpage
2481
Abstract
In physiological control there is a need to estimate signals that cannot be measured directly. Burdened by measurement noise and unknown disturbances this proves to be challenging, since the models are usually highly nonlinear. Sigma-point filters could represent an adequate choice to overcome this problem. The paper investigates the applicability of several different versions of sigma-point filters for the Artificial Pancreas problem on the widely used Cambridge (Hovorka)-model.
Keywords
diseases; estimation theory; filtering theory; medical signal processing; Cambridge model; Hovorka model; artificial pancreas problem; diabetes model; measurement noise; physiological control; sigma-point filter; state estimation; Conferences; Cybernetics;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on
Conference_Location
San Diego, CA
Type
conf
DOI
10.1109/SMC.2014.6974298
Filename
6974298
Link To Document