Optimized Neural Network Method for Temperature Estimation along Optical Fiber
【Objective】To effectively estimate the optical fiber temperature of distributed optical fiber sensing based on Brillouin scattering, a multi-layer feedforward Artificial Neural Network (ANN) is introduced to estimate the temperature.【Methods】The article has developed programs in Matlab for fiber opt...
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《光通信研究》编辑部
2025-02-01
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| Series: | Guangtongxin yanjiu |
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| Online Access: | http://www.gtxyj.com.cn/thesisDetails#10.13756/j.gtxyj.2025.230123 |
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| _version_ | 1849774888323121152 |
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| author | LI Suya DONG Yanwei LI Lin ZHANG Chi LI Nan NING Qi CHEN Yonghui |
| author_facet | LI Suya DONG Yanwei LI Lin ZHANG Chi LI Nan NING Qi CHEN Yonghui |
| author_sort | LI Suya |
| collection | DOAJ |
| description | 【Objective】To effectively estimate the optical fiber temperature of distributed optical fiber sensing based on Brillouin scattering, a multi-layer feedforward Artificial Neural Network (ANN) is introduced to estimate the temperature.【Methods】The article has developed programs in Matlab for fiber optic temperature calculation using the single slope method, the least squares fitting method based on the pseudo-Voigt model，and an ANN. At the same time, the Brillouin spectra with different Signal-to-Noise Ratios (SNR) are simulated. Based on the Brillouin spectrum generated from the above simulations, the study investigates the key parameters of the ANN, namely the number of hidden layers, the number of neurons in the hidden layers, and the training objectives, and their influence on training speed, temperature calculation time, and accuracy.【Results】The maximum temperature error of ANN is only 1.18 and 0.63 ℃ at 22 and 37 dB respectively, and the calculation time of ANN is only about 1/1 000 of that of the least-squares fit method. When the number of hidden layer neurons remains constant，the training time decreases obviously with the number of hidden layer and the computation time increases linearly with the number of hidden layer. However, it has little effect on the accuracy of temperature estimation. Both the training time and computation time increase with the number of neurons in the hidden layer. When there are 21 neurons in the hidden layer, the training time is approximately 67 times that of only one neuron in the hidden layer. However, it also has little effect on the accuracy of temperature estimation. When the training goal (square of the Brillouin shift error) is less than the critical value (about 1 MHz<sup>2</sup>), the temperature error is almost independent. However, when the training goal exceeds the critical value, the temperature error increases with the training goal.【Conclusion】When ANN is used to estimate optical fiber temperature of distributed optical fiber sensing based on Brillouin scattering, it is recommended to select a single-hidden-layer ANN and the number of hidden layer neurons is set to one. The training goal is set to 1 MHz<sup>2</sup>. |
| format | Article |
| id | doaj-art-e0046edbad3244d38522dc696f3d7f58 |
| institution | DOAJ |
| issn | 1005-8788 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | 《光通信研究》编辑部 |
| record_format | Article |
| series | Guangtongxin yanjiu |
| spelling | doaj-art-e0046edbad3244d38522dc696f3d7f582025-08-20T03:01:35Zzho《光通信研究》编辑部Guangtongxin yanjiu1005-87882025-02-01230123-0682629916Optimized Neural Network Method for Temperature Estimation along Optical FiberLI SuyaDONG YanweiLI LinZHANG ChiLI NanNING QiCHEN Yonghui【Objective】To effectively estimate the optical fiber temperature of distributed optical fiber sensing based on Brillouin scattering, a multi-layer feedforward Artificial Neural Network (ANN) is introduced to estimate the temperature.【Methods】The article has developed programs in Matlab for fiber optic temperature calculation using the single slope method, the least squares fitting method based on the pseudo-Voigt model，and an ANN. At the same time, the Brillouin spectra with different Signal-to-Noise Ratios (SNR) are simulated. Based on the Brillouin spectrum generated from the above simulations, the study investigates the key parameters of the ANN, namely the number of hidden layers, the number of neurons in the hidden layers, and the training objectives, and their influence on training speed, temperature calculation time, and accuracy.【Results】The maximum temperature error of ANN is only 1.18 and 0.63 ℃ at 22 and 37 dB respectively, and the calculation time of ANN is only about 1/1 000 of that of the least-squares fit method. When the number of hidden layer neurons remains constant，the training time decreases obviously with the number of hidden layer and the computation time increases linearly with the number of hidden layer. However, it has little effect on the accuracy of temperature estimation. Both the training time and computation time increase with the number of neurons in the hidden layer. When there are 21 neurons in the hidden layer, the training time is approximately 67 times that of only one neuron in the hidden layer. However, it also has little effect on the accuracy of temperature estimation. When the training goal (square of the Brillouin shift error) is less than the critical value (about 1 MHz<sup>2</sup>), the temperature error is almost independent. However, when the training goal exceeds the critical value, the temperature error increases with the training goal.【Conclusion】When ANN is used to estimate optical fiber temperature of distributed optical fiber sensing based on Brillouin scattering, it is recommended to select a single-hidden-layer ANN and the number of hidden layer neurons is set to one. The training goal is set to 1 MHz<sup>2</sup>.http://www.gtxyj.com.cn/thesisDetails#10.13756/j.gtxyj.2025.230123distributed optical fiber sensorBrillouin scatteringBrillouin frequency shiftANNtemperatureoptimization |
| spellingShingle | LI Suya DONG Yanwei LI Lin ZHANG Chi LI Nan NING Qi CHEN Yonghui Optimized Neural Network Method for Temperature Estimation along Optical Fiber Guangtongxin yanjiu distributed optical fiber sensor Brillouin scattering Brillouin frequency shift ANN temperature optimization |
| title | Optimized Neural Network Method for Temperature Estimation along Optical Fiber |
| title_full | Optimized Neural Network Method for Temperature Estimation along Optical Fiber |
| title_fullStr | Optimized Neural Network Method for Temperature Estimation along Optical Fiber |
| title_full_unstemmed | Optimized Neural Network Method for Temperature Estimation along Optical Fiber |
| title_short | Optimized Neural Network Method for Temperature Estimation along Optical Fiber |
| title_sort | optimized neural network method for temperature estimation along optical fiber |
| topic | distributed optical fiber sensor Brillouin scattering Brillouin frequency shift ANN temperature optimization |
| url | http://www.gtxyj.com.cn/thesisDetails#10.13756/j.gtxyj.2025.230123 |
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