Design scheme of second-order Raman fiber amplifier based on CNN-LSTM and Sea-horse optimizer algorithm

We propose a second-order Raman fiber amplifier gain and noise co-prediction model combining convolutional neural network and long short-term memory network to study the influence of different LSTM layers on the performance of the prediction model, and the optimal parameter configuration model was obtained by optimizing with the Seahorse Algorithm. The model can accurately reflect the mapping relationship between pumping parameters, fiber length, target gain, and noise distribution, and effectively improves the design efficiency and performance of Raman fiber amplifiers. The experimental results show that the root mean square error of the finally established SHO-CNN-LSTM model in terms of gain and noise prediction is only 0.0431 and 0.0224dB, the error between the predicted value and the target value does not exceed 0.26dB, and the average design time does not exceed 0.0002s. This design scheme provides the best design methods and ideas for the flexible and fast design of future Raman fiber amplifiers.