DocumentCode :
2428952
Title :
Nonparametric modeling of glucose-insulin process in IDDM patient using Hammerstein-Wiener model
Author :
Bhattacharjee, Arpita ; Sengupta, Anindita ; Sutradhar, Ashoke
Author_Institution :
Dept. of Electr. Eng., Bengal Eng. & Sci. Univ., Howrah, India
fYear :
2010
fDate :
7-10 Dec. 2010
Firstpage :
2266
Lastpage :
2271
Abstract :
This paper deals with an identification problem of modeling a nonlinear dynamic system of multivariable glucose-insulin process in an IDDM patient. Out of many model structures that can represent a nonlinear process effectively; the Hammerstein-Wiener model has attracted a lot of attention. The present work proposes a generalized identification method of Hammerstein-Wiener model from the input-output data of multivariable nonlinear glucose-insulin process. The present algorithm consists of a three-block (LNL) realization. For the multivariable system, the first and third blocks are standard impulse response filter (TRF) realization applied to an equivalent linear system using adaptive recursive least square (ARLS) algorithms. In the second block, i.e. the nonlinear part, ARLS algorithms have been used to solve up to second order kernels of Volterra equations with extended input vector consisting of cross components as well. The input-output data taken from the simulated nonlinear process have been used to identify the system with a filter memory length of M=3 and the validation results have shown good fit and in concordance with predicted output.
Keywords :
Volterra equations; filtering theory; least squares approximations; linear systems; medical control systems; multivariable systems; nonlinear dynamical systems; recursive estimation; stochastic processes; Hammerstein-Wiener model; Volterra equations; adaptive recursive least square algorithms; equivalent linear system; generalized identification method; insulin dependent diabetes mellitus patient; multivariable glucose-insulin process; multivariable system; nonlinear dynamic system; nonparametric modeling; second order kernels; standard impulse response filter realization; three-block realization; Adaptive filters; Data models; Insulin; Kernel; Nonlinear dynamical systems; Nonlinear filters; Sugar; Hammerstein-Wiener Model; Volterra kernels; glucose-insulin interaction; nonparametric model; system identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-7814-9
Type :
conf
DOI :
10.1109/ICARCV.2010.5707385
Filename :
5707385
Link To Document :
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