Research on Raman fiber amplifier using neural network combining PSO algorithm

Abstract

We propose an efficient hybrid method that combines neural network and particle swarm optimization algorithm to optimize the performance of backward multi-pumped Raman fiber amplifiers. We use a neural network to inverse system design Raman fiber amplifier by learning the nonlinear mapping relationship between pump light and the output gain. To obtain a flat gain spectrum, the particle swarm optimization algorithm is used to search for the optimal pump slight parameter configuration. The results show that when the designed Raman amplifier is oriented toward C+L band signal optical amplification, the error between the target gain value and the actual gain value is less than 0.47 dB, the output gain after optimization is 17.96dB, and the gain flatness is 0.44dB.