基于混合机器学习的电磁功率谱密度预测模型

Electromagnetic Power Spectrum Density prediction model based on hybrid machine learning

摘要:

功率谱密度(PSD)预测是频谱管理中的重要环节。由于功率谱密度具有高度的复杂性、非线性和不确定性，单一的预测模型很难确保预测的准确性和效率。为克服单一预测方法的不足，提出一种混合的机器学习模型，将自组织映射(SOM)网络与回归树(RT)相结合，以预测信号的功率谱密度。使用自组织映射网络将具有相似手工特征的原始样本集聚类成簇；将每一个簇分别构建回归树来预测功率谱密度；最后，使用亚琛工业大学的数据进行实验。结果表明，预测结果的均方根误差比现有方法提高0.824，证明混合模型具有较高的预测精确度和较好的泛化能力。

Abstract:

Power Spectral Density(PSD) prediction is an important part of spectrum management. Due to the high complexity, nonlinearity and uncertainty of the PSD, it is difficult for a single prediction model to ensure the accuracy and efficiency of the prediction. In order to overcome the disadvantages of a single prediction method, a hybrid machine learning model is proposed to combine a Self-Organizing Map(SOM) network with a Regression Tree(RT) to predict the PSD of the signal. First, the method uses a self-organizing map network to cluster the original sample sets with similar manual features. Then, a RT is constructed for each cluster to predict the PSD. Finally, the data of RWTH from Aachen University are adopted for experiments. The root mean square error of the prediction result is 0.824 higher than that of the existing method, which proves that the hybrid model has higher prediction accuracy and better generalization ability.