Application of Neural Network Algorithms for Central Wavelength Determination of Fiber Optic Sensors

Abstract

Fiber Bragg gratings are sensitive elements in fiber optic sensor networks, and this paper discusses the practicalities of using neural network algorithms to determine their central wavelengths. The problem is to determine the central wavelength of a single sensor, the parameters of which are obtained using a low-resolution spectrum analyzer. The configuration of the neural network and the algorithm for producing the training and control datasets are specified. The training results for the selected neural network configuration demonstrated that the proposed method could determine the position of the central wavelength with a resolution two and a half orders of magnitude higher than the resolution of the input data sampling. The obtained results demonstrate that the approach makes it possible to determine the FBG central wavelength shift with an error not exceeding ~0.5 pm at a spectrum analyzer resolution of 167 pm.

Keywords

• spectrum shift determination
• neural network
• stochastic optimization
• Adam optimization method
• artificial intelligence
• fiber optics sensor