通过构建人工神经网络模型估算中国西北高原地区近地面光学湍流。对多层感知器(MLP)的结构进行优化,其输入层包括10个特征,隐含层包括40个神经元。探讨已建多层感知器的性能,结果表明:当训练集和测试集来自同一地区时,模型的平均相对误差为1.34%,折射率结构常数的实测值和估计值的拟合优度为0.94;当训练集和测试集来自不同地区时,多层感知器的泛化能力需进一步提高。

This paper presents an estimate of surface layer optical turbulence in Northwest China using an artificial neural network. We optimize the configuration of the multilayer perceptron (MLP), including 10 features in the input layer and 40 neurons in the hidden layer. The performance of the constructed MLP is investigated. The results show that when the training set and testing set are from the same site, the mean relative error of the model is 1.34%. The goodness of fit between measured and estimated refractive index structure constants is 0.94. We propose that when the training set and testing set come from different sites, the generalization ability of the MLP should be enhanced.