Name
Abstract | ||
|---|---|---|
System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity o... |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TBME.2007.896593 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
System identification,Signal processing,Parameter estimation,State estimation,Predictive models,Numerical simulation,Performance evaluation,Least squares methods,Noise measurement,Connective tissue | Least squares,Signal processing,Noise measurement,Computer simulation,Control theory,Computer science,Minimisation (psychology),Artificial intelligence,Estimation theory,System identification,Computer vision,Algorithm,State space | Journal |
Volume | Issue | ISSN |
54 | 12 | 0018-9294 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sean R. Anderson | 1 | 89 | 14.87 |
| Paul Dean | 2 | 93 | 10.90 |
| Visakan Kadirkamanathan | 3 | 0 | 0.34 |
| Chris R. S. Kaneko | 4 | 3 | 0.79 |
| J. Porrill | 5 | 195 | 139.71 |