Spacecraft electrical characteristics identification method based on PCA feature extraction and WPSVM

To solve the problems of large amount of unlabeled test data, high dimension characteristics, slow computing speed and low recognition rate during the spacecraft electrical characteristics identification process of monitoring system, an on-line identification algorithm based on principal component analysis (PCA) feature extraction and weighted proximal support vector machine (WPSVM) was proposed. The principal component analysis is used for feature selection and extraction during complex signal analysis process, to reduce the characteristics dimension and improve the speed of the spacecraft electrical on-line identification. In order to resolve the PCA results selection problem, our team put forward data capture contribution method by using threshold to capture data, effectively guarantee the validity and consistency of the data. The experimental results indicate that this method we proposed can get better spacecraft electrical characteristics data feature, improve the accuracy of identification, and shorten the compute-time with high efficiency at the same time.