M. Noshiro, H. Shindou, Y. Fukuoka, M. Ishikawa, H. Minanitani, K. Sakamoto, A. Tanakadate, S. Nebuya
{"title":"用神经网络计算估计了人类pCO/sub 2/控制系统的NARMAX模型","authors":"M. Noshiro, H. Shindou, Y. Fukuoka, M. Ishikawa, H. Minanitani, K. Sakamoto, A. Tanakadate, S. Nebuya","doi":"10.1109/IEMBS.1996.647616","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":20427,"journal":{"name":"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1996-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NARMAX model of the pCO/sub 2/ control system in man estimated by neural computation\",\"authors\":\"M. Noshiro, H. Shindou, Y. Fukuoka, M. Ishikawa, H. Minanitani, K. Sakamoto, A. Tanakadate, S. Nebuya\",\"doi\":\"10.1109/IEMBS.1996.647616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":20427,\"journal\":{\"name\":\"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMBS.1996.647616\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1996.647616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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