Name
Abstract | ||
|---|---|---|
Aimed at the calibration problem of multi-sensor system because of cross-sensitivity, a novel approach based on forward-model and inverse-model was proposed. For forward-model, measurants were taken as inputs, sensor outputs were taken as outputs. For inverse-model, its inputs and outputs were just the opposite. The forward-model was built with calibration samples by interpolation at first. And with this model, "additional samples" were produced. Then, inverse model was built with calibration samples and "additional samples" by training neural networks for every component of analyte. Finally, spectral analysis of light alkane gas mixture was taken as an example to test the calibration approach. The results showed that almost same accuracy, when the approach was applied, could be obtained with only 200 sets of samples as that obtained with 7000 sets of samples, which meaned the proposed approach could reduce the number of calibration sample. And calibration cost could be reduced. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-52015-5_25 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Multi-sensor system,Cross-sensitivity,Calibration approach,Neural network,Additional samples | Conference | 10135 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.63 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojun Tang | 1 | 1 | 1.98 |
| Feng Zhang | 2 | 2 | 0.99 |
| Hailin Zhang | 3 | 1 | 0.63 |
| Junhua Liu | 4 | 2 | 1.34 |
| Yuntao Liang | 5 | 1 | 0.63 |