Temperature demodulation for optical fiber F-P sensor based on DBNs with ensemble learning

Aiming at the problem of traditional temperature demodulation due to ignoring the irregular change of the thermo-optical and thermal expansion coefficients, a demodulation method based on deep belief networks (DBNs) with ensemble learning is proposed. The DBN can establish a nonlinear mapping model between spectrum and temperature to learn the information that the thermo-optic and thermal expansion coefficient are changed with temperature, the accurate temperature demodulation can be realized. In order to improve the learning ability of the individual model, a stacking ensemble method is utilized to learn the knowledge of five DBN models with different activation functions. The proposed method achieves the demodulation performance that the demodulation precision is 0.30%F.S., and the mean absolute error is as low as 0.98 degrees C in the range of temperature from 30 degrees C to 1100 degrees C. It shows that the proposed method can promote the learning ability of algorithm to improve the demodulation performance of single DBN model and make F-P high temperature sensing system more accurate and reliable.