介绍了一种将反向传播(BP)神经网络算法与人工蜂群算法相结合的方法,并用该方法对多泵浦拉曼光纤放大器的设计进行了优化。通过研究多层BP神经网络中的隐藏层层数和神经节点数,确定了最佳的学习模型,该模型可以精准地反映泵浦波长和泵浦功率与拉曼净增益分布间的映射关系,能代替传统求解拉曼耦合波方程的方法。同时,为了提高增益谱的平坦性,采用人工蜂群算法来优化泵浦参数,得到了最优的泵浦波长和泵浦功率。仿真结果表明,通过将训练好的BP神经网络模型加入到人工蜂群算法中,所研究的拉曼放大器达到了期望的增益性能,且其目标值与预测值的最大误差不超过0.29 dB。该设计方案为拉曼光纤放大器的研究提供了新的思路和方法。

A method combining a back propagation (BP) neural network algorithm with the artificial bee colony algorithm is introduced, and the design of multi-pump Raman fiber amplifier is optimized by this method. The best learning model is determined by studying the numbers of hidden layers and neural nodes in the multilayer BP neural network, which can accurately reflect the mapping relationships of the pump wavelength and pump power with the distribution of Raman net gain, and can replace the traditional method for solving the Raman coupled wave equation. At the same time, in order to improve the flatness of the gain spectrum, the artificial bee colony algorithm is used to optimize the pump parameters and the optimal pump wavelength and pump power are obtained. The simulation results show that when the trained BP neural network model is added into the artificial bee colony algorithm, the desired gain performance of the studied Raman amplifier is achieved. Moreover, the maximum error between the target value and the predicted value is less than 0.29 dB. This design scheme provides a new method and idea for the study of Raman fiber amplifiers.