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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。
Aiming at the problems such as large amount of data, high characteristic dimension, large computational complexity and low recognition rate, the electrical characteristic signal data of the spacecraft presented the feature extraction method based on principal component analysis (PCA) and random forest (RF) algorithm, Dimensionality reduction, increase calculation efficiency and recognition rate, and realize rapid and accurate identification and classification of spacecraft electrical signal data. However, considering the time complexity problem, the stochastic forest algorithm uses the principal component analysis method to compress and reduce the dimensionality of the data, so as to ensure the accuracy and improve the computational efficiency. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency and stability for spacecraft signal characteristics.