Intensity and Wavelength-Division Multiplexing Fiber Sensor Interrogation Using a Combination of Autoencoder Pre-Trained Convolution Neural Network and Differential Evolution Algorithm

This paper proposes a new fiber Bragg grating central wavelength interrogation system by combining evolutionary algorithm and machine learning techniques integrated with an unsupervised autoencoder (AE) pre-training strategy. The proposed unsupervised AE pre-training convolution neural network (CNN) allows training of the convolutional layers independently from a regression task in order to learn a new data representation and give better generalization. It is also used to improve the system accuracy by four times without extra-labeled data. Moreover, AE is combined with a differential evolutionary (DE) algorithm to automate the human labeling task. The proposed autoencoder pre-training convolution neural network and differential evolutionary (AECNNDE) interrogation system achieve good accuracy and can speed-up the computational time by a maximum of 30 times than DE algorithm.

Automated summary

The paper proposes a new fiber Bragg grating central wavelength interrogation system by combining an unsupervised autoencoder pre-training convolutional neural network with a differential evolutionary algorithm. This system achieves good accuracy and can speed up computational time by a maximum of 30 times compared to the DE algorithm.