1999 Fiscal Year Final Research Report Summary
Identification of the pCOィイD22ィエD2 control system using a nonlinear autoregressive moving average model
| Project/Area Number |
09680861
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Biomedical engineering/Biological material science
|
|---|
| Research Institution | Kitasato University |
|---|
Principal Investigator |
NOSHIRO Makoto Kitasato Univerisity, School of Allied Health Sciences , Professor, 医療衛生学部, 教授 (80014231)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
TANAKADATE Akihiro Kitasato Univerisity, School of Allied Health Sciences , Associate Professor, 医療衛生学部, 助教授 (20265747)
SAKAMOTO Katsuyuki Kitasato Univerisity, School of Allied Health Sciences , Associate Professor, 医療衛生学部, 助教授 (50053674)
WATANABE Satoshi Kitasato Univerisity, School of Allied Health Sciences , Professor, 医療衛生学部, 教授 (40050463)
NEBUYA Satoru Kitasato Univerisity, School of Allied Health Sciences , Instructor, 医療衛生学部, 助手 (00276180)
|
|---|
| Project Period (FY) |
1997 – 1999
|
| Keywords | System identification / nonlinear system / NARMAX model / neural network / partial pressure of COィイD22ィエD2 / respiratory system |
|---|
| Research Abstract |
Three mass-flow controllers operated by a personal computer made a gas mixture of NィイD22ィエD2, OィイD22ィエD2 and COィイD22ィエD2, which was given to subjects. Respiratory flow and pCOィイD22ィエD2 was recorded so that minute volume (input of the system) and end-tidal pCOィイD22ィエD2 (output of the system) could be obtained. These two variables were used for system identification. The previously proposed method for system identification was improved: the slope of sigmoid functions in the neural network was varied for each unit, the sigmoid functions were expanded around the threshold in place of zero and a model for noises was added to the neural network model.
|
