基于SSA ELM和自适应差分进化算法的拉曼放大器设计

Design of Raman amplifier based on SSA ELM andadaptive differential evolution algorithm

提出了一种将樽海鞘群算法优化极限学习机与自适应差分进化算法相结合的方法,并利用该方法优化多泵浦拉曼光纤放大器的参数配置。采用极限学习机构建泵浦参数和拉曼增益之间的非线性映射,并利用樽海鞘群优化算法对极限学习机参数进行优化获得最佳模型。对比分析了上述模型与BP神经网络和传统的极限学习机模型在评价指标方面的差异,结果表明本文所提出的模型预测性能较好。为了提高增益平坦性,利用自适应差分进化算法优化泵浦参数,得到最佳的参数配置。仿真结果表明,利用该方法设计出的拉曼放大器达到了预期效果,其目标增益与预测增益的最大误差不超过05dB。该方法为今后拉曼光纤放大器的设计提供了一种新的思路方法。

In this paper,a method that combines the salp swarm optimization algorithm with extreme learning machine and adaptive differential evolution algorithm is proposed to optimize the parameter configuration of the multi pump Raman fiber amplifier.The nonlinear mapping between pump parameters and Raman gain is constructed by extreme learning machine,and the optimal model is obtained by optimizing the parameters of extreme learning machine by using salp group optimization algorithm.The differences between the above model and BP neural network and the traditional extreme learning machine model in terms of evaluation indicators are compared and analyzed,and the results show that the model proposed in this paper has better prediction performance.In order to improve the gain flatness,an adaptive differential evolution algorithm is used to optimize the pump parameters to obtain the best parameter configuration.The simulation results show that the Raman amplifier designed by this method achieves the expected effect,and the maximum error between the target gain and the predicted gain does not exceed 0.5 dB.This method provides a new way of thinking for the design of Raman fiber amplifiers in the future.