用神经网络计算估计了人类pCO/sub 2/控制系统的NARMAX模型

Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Pub Date : 1996-10-31 DOI:10.1109/IEMBS.1996.647616

M. Noshiro, H. Shindou, Y. Fukuoka, M. Ishikawa, H. Minanitani, K. Sakamoto, A. Tanakadate, S. Nebuya

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引用次数: 0

摘要

受试者自愿激发一种气体混合物，其中CO/sub 2/浓度逐步或随机变化。测量吸入气体和呼出气体中的呼吸流量和pCO/sub 2/，得到潮汐末pCO/sub 2/和分钟通气量，分别作为pCO/sub 2/控制系统的输入和输出。利用三层前馈神经网络估计了系统的非线性自回归移动平均(NARMAX)模型。估计模型包含y(t-1)， x(t-1)， x(t-2)， x /sup 2/(t-2)和y(t-1)x(t-2)项。从用于估计的数据中计算出的非线性度量表明，大多数对象的pCO/sub /控制系统具有不可忽视的非线性。

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NARMAX model of the pCO/sub 2/ control system in man estimated by neural computation

Subjects voluntarily inspire a gas mixture in which the CO/sub 2/ concentration is changed stepwise or randomly. The respiratory flow rate and pCO/sub 2/ in the inspired and expired gases are measured to yield the end-tidal pCO/sub 2/ and minute ventilation, which are the input and output of the pCO/sub 2/ 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), X/sup 2/(t-2) and y(t-1)x(t-2). A measure of nonlinearity calculated from the data used for estimation shows the pCO/sub 2/ control system in most subjects has a nonlinearity which cannot be neglected.

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Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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