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Gain adaptive tuning method for fiber Raman amplifier based on two-stage neural networks and double weights updates
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作者 MU Kuanlin WU Yue 《Optoelectronics Letters》 2025年第5期284-289,共6页
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph... We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy. 展开更多
关键词 gain adaptive tuning connection weights error predicted target gains training connection weights unified nn gain adaptive tuning method double weights updates fiber raman amplifier fra
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