The Multiple Classification Method of Signal Recognition for Spacecraft Based on SAE Network

摘要

Based on deep learning, a multi-classification algorithm network is designed for the large amount of data generated in spacecraft test. In the algorithm, the initial offsets and weights of a multi-layer neural network are initialized using an auto-encoder method. The initialized parameters are monitored by the gradient descent method to make the dimension data more separable. Many shortcomings of traditional algorithms can be effectively overcome using this algorithm. For example, the storage space can be reduced and the calculation time can be saved when the data is large or complex. Expert knowledge of the spacecraft health management platform can be provided through the study of measured data. Experimental data shows that the depth learning algorithm which is based on SAE has higher accuracy in spacecraft multi-class signal testing.