Nonlinearity identified by neural network models inP CO 2 control system in humans |
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| Authors: | Dr. Y. Fukuoka M. Noshiro H. Shindo H. Minamitani M. Ishikawa |
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| Affiliation: | (1) Division of Electronic Engineering, Institute for Medical and Dental Engineering, Tokyo Medical and Dental University, 2-3-10 Kanda-Surugadia, Chiyoda-ku, 101 Tokyo, Japan;(2) Department of Clinical Engineering, School of Allied Health Sciences, Kitasato University, Japan;(3) Department of Electrical Engineering, Faculty of Science and Technology, Kefo University, Japan;(4) Department of Control System Engineering, College of Computer Science and System Engineering, Kyushu Institute of Technology, Japan |
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| Abstract: | The nonlinearity included in the PCO 2 control system in humans is evaluated using the degree of nonlinearity based on a difference of residuals. An autoregressive moving average (ARMA) model and neural networks (linear and nonlinear) are employed to model the system, and three types of network (Jordan, Elman and fully interconnected) are compared. As the Jordan-type linear network cannot approximate respiratory data accurately, the other two types and the ARMA model are used for the evaluation of the nonlinearity. The results of the evaluation indicate that the linear assumption for the PCO 2 control system is invalid for three subjects out of seven. In particular, strong nonlinearity was observed for two subjects. |
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| Keywords: | Back-propagation learning algorithm Identification Neural network Nonlinearity Respiratory system PCO 2 |
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