TY - JOUR
T1 - Identification of a nonlinear model for a glucoregulatory benchmark problem
AU - Vanbeylen, Laurent
AU - Van Mulders, Anne
AU - Abu-Rmileh, Amjad
N1 - Funding Information:
Laurent Vanbeylen and Anne Van Mulders's work was supported by the Fund for Scientific Research (FWO-Vlaanderen) , the Flemish Government (Methusalem Grant METH-1) , the Belgian Program on Inter-university Poles of Attraction (IAP VII/19 – Dysco), and by the ERC advanced grant SNLSID , under contract 320378. Amjad Abu-Rmileh acknowledges the support of the University of Girona through the BR-UdG research grant.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Recently, a novel identification method for a nonlinear dynamic model, called nonlinear Linear Fractional Representation (NL-LFR) model, has been developed. The model, composed of a static nonlinearity (SNL) surrounded by linear dynamics, can account for both nonlinear feed-forward and nonlinear feed-back effects. Using two classical frequency response measurements, the SNL is automatically recovered in a user-friendly and efficient (non-iterative) way. In this contribution, the method is illustrated on a glucoregulatory benchmark dataset (insulin-glucose relationship of the human body). The research on insulin-glucose models is essential to develop methodologies to control the blood glucose level in diabetes patients. The obtained results outperform earlier results on the same benchmark data, while providing an excellent accuracy-complexity tradeoff.
AB - Recently, a novel identification method for a nonlinear dynamic model, called nonlinear Linear Fractional Representation (NL-LFR) model, has been developed. The model, composed of a static nonlinearity (SNL) surrounded by linear dynamics, can account for both nonlinear feed-forward and nonlinear feed-back effects. Using two classical frequency response measurements, the SNL is automatically recovered in a user-friendly and efficient (non-iterative) way. In this contribution, the method is illustrated on a glucoregulatory benchmark dataset (insulin-glucose relationship of the human body). The research on insulin-glucose models is essential to develop methodologies to control the blood glucose level in diabetes patients. The obtained results outperform earlier results on the same benchmark data, while providing an excellent accuracy-complexity tradeoff.
KW - Artificial pancreas
KW - Best linear approximation
KW - Black-box modelling
KW - Insulin-glucose modelling
KW - Nonlinear fractional representation
KW - Nonlinear models
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=84901856621&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2014.04.007
DO - 10.1016/j.bspc.2014.04.007
M3 - Article
AN - SCOPUS:84901856621
SN - 1746-8094
VL - 13
SP - 168
EP - 173
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
IS - 1
ER -