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10 October 2020 Temperature prediction by helical microfiber sensors based on artificial neural network
Juan Liu, Xining Zhang, Yaping Zhang, Mengjie Li, Hang Yu
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Proceedings Volume 11554, Advanced Sensor Systems and Applications X; 115540F (2020) https://doi.org/10.1117/12.2573199
Event: SPIE/COS Photonics Asia, 2020, Online Only
Abstract
In this paper, we propose a back propagation neural network (BPNN) for temperature forecasting by helical microfiber sensors. The structural parameters, such as the microfiber diameter, the tapered angle, the input and output offset angle, the waist length and the helical angle, are considered as the input parameters of the network for sensing the temperature (T). 758 transmitted intensity (I)-T data pairs obtained from over 38 helical microfiber sensors are used for the network training. The prediction ability of the model is evaluated by root-mean-square error (RMSE). Compared with the fitting curve based on the measured I-T data, the neural network can directly predict the temperature according to the training model with RMSE of 0.6033.In addition, the major structural parameters are determined by comparing the prediction performances of the networks with different inputs.
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Juan Liu, Xining Zhang, Yaping Zhang, Mengjie Li, and Hang Yu "Temperature prediction by helical microfiber sensors based on artificial neural network", Proc. SPIE 11554, Advanced Sensor Systems and Applications X, 115540F (10 October 2020); https://doi.org/10.1117/12.2573199
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KEYWORDS
Sensors
Artificial neural networks
Data modeling
Neural networks
Temperature metrology
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