Name
Abstract | ||
|---|---|---|
Building a precise respiration system model is very helpful for setting appropriate ventilation conditions to fit each patient when artificial respiration is performed on the patient. In this paper, a new respiration system model is proposed, which is a second order nonlinear differential equation including volume dependent elastic term described by RBF network. The model is able to describe the nonlinear dynamics of respiration. By using Sagara's numerical integration technique, a discrete-time identification model is derived. Then, off-line and online parameter estimation algorithms are presented. It is easy to obtain pulmonary elastance from identified model. The proposed model and the parameter estimation method are validated by clinical examples. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28648-6_81 | ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 2 |
Keywords | Field | DocType |
system modeling,discrete time,nonlinear dynamics,second order,numerical integration,parameter estimation | Radial basis function network,Radial basis function,Nonlinear system,Respiration,Computer science,Control theory,Numerical integration,Estimation theory,System model,Elastance | Conference |
Volume | ISSN | Citations |
3174 | 0302-9743 | 2 |
PageRank | References | Authors |
0.72 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shunshoku Kanae | 1 | 78 | 11.91 |
| Zi-jiang Yang | 2 | 2 | 0.72 |
| Kiyoshi Wada | 3 | 85 | 11.45 |