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.
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All the samples can be downloaded from my network disk: http://pan.baidu.com/s/1hqrKdJe.
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Acknowledgments
The authors gratefully acknowledge financial support by the National Natural Science Foundation of China (51277144) and the National Major Project on Scientific Instrument Development of China (2012YQ240127).
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Tang, X., Zhang, F., Zhang, H., Liu, J., Liang, Y. (2017). Multi-sensor System Calibration Approach Based on Forward-Model and Inverse-Model. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_25
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DOI: https://doi.org/10.1007/978-3-319-52015-5_25
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-52015-5
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