Title :
NARMAX model of the pCO2 control system in man estimated by neural computation
Author :
Noshiro, Makoto ; Shindou, Hiroyuki ; Fukuoka, Yutaka ; Ishikawa, Masumi ; Minanitani, Haruyuki ; Sakamoto, Katsuyuki ; Tanakadate, Akihiro ; Nebuya, Satoru
Author_Institution :
Sch. of Allied Health Sci., Kitasato Univ., Sagamihara, Japan
fDate :
31 Oct-3 Nov 1996
Abstract :
Subjects voluntarily inspire a gas mixture in which the CO2 concentration is changed stepwise or randomly. The respiratory flow rate and pCO2 in the inspired and expired gases are measured to yield the end-tidal pCO2 and minute ventilation, which are the input and output of the pCO2 control system, respectively. A NARMAX (Nonlinear Auto-Regressive Moving Average with eXogeneous inputs) model of the system is estimated using a three-layered feedforward neural network. The estimated model contains terms, y(t-1), x(t-1), x(t-2), X2(t-2) and y(t-1)x(t-2). A measure of nonlinearity calculated from the data used for estimation shows the pCO2 control system in most subjects has a nonlinearity which cannot be neglected
Keywords :
biocontrol; carbon compounds; feedforward neural nets; physiological models; pneumodynamics; CO2; NARMAX model; expired gases; inspired gases; man; minute ventilation; neural computation; nonlinearity; pCO2 control system; three-layered feedforward neural network; Autoregressive processes; Control system synthesis; Control systems; Dentistry; Feedforward neural networks; Fluid flow measurement; Neural networks; Nonlinear control systems; Space technology; Ventilation;
Conference_Titel :
Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE
Conference_Location :
Amsterdam
Print_ISBN :
0-7803-3811-1
DOI :
10.1109/IEMBS.1996.647616
