%0 Journal Article
%T 基于随机森林的航天器电信号多分类识别方法<br>Multi-classification spacecraft electrical signal identification method based on random forest
%A 兰巍
%A 贾素玲
%A 宋世民
%A 李可
%J 北京航空航天大学学报
%D 2017
%R 10.13700/j.bh.1001-5965.2016.0661
%X 摘要 针对航天器电特性信号数据存在数据量大、特征维数高、计算复杂度大和识别率低等问题，提出基于主成分分析（PCA）的特征提取方法和随机森林（RF）算法，对原始数据进行降维，提高计算效率和识别率，实现对航天器电信号数据的快速、准确识别分类。随机森林算法在处理高维数据上具有优越的性能，但是考虑到时间复杂度问题，利用主成分分析方法对数据进行压缩和降维，在保证准确率的同时提高了计算效率。实验结果表明：与其他算法相比，针对航天器电特性信号数据，本文方法在准确率、计算效率和稳定性等方面均显示出优异的性能。<br>Abstract：The spacecraft electrical signal characteristic data have problems such as large amount, high-dimensional features, high computational complexity and low identification rate. The feature extraction method of principal component analysis (PCA) and random forest (RF) algorithm was proposed to reduce the dimensionality of the original data, improve the computational efficiency and identification rate, and achieve rapid and accurate identification of spacecraft electrical signal data. The random forest algorithm has superior performance in dealing with high-dimensional data. However, considering the time complexity, the method of PCA was used to compress the data and reduce the dimension in order to ensure the accuracy of the classification and improve the computational efficiency. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in accuracy, computational efficiency, and stability when dealing with spacecraft electrical signal data.
%K 航天器
%K 电信号识别
%K 主成分分析(PCA)
%K 多分类
%K 随机森林(RF)&lt
%K br&gt
%K spacecraft
%K electrical signal identification
%K principal component analysis (PCA)
%K multi-classification
%K random forest (RF)
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14398.shtml