Abstract
In this paper, we introduce the embedded learning idea in the learning model to design a few-shot learning algorithm based on convolution denoising auto-encoder relational network. The purpose is to solve the problem of high cost and small amount of data during the spacecraft’s orbit operation. By building the end-to-end relationship network and learning relationships between each sample, we can diagnose the fault of the spacecraft thermal control system effectively with few samples. The experimental results show that the classification algorithm in this paper has a significant improvement in accuracy compared with traditional deep learning methods, and avoid overfitting problem effectively.
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Acknowledgements
This work are supported by the Chinese National Natural Science Foundation (No. 61773039), the Aeronautical Science Foundation of China (No. 2017ZDXX1043), and Aeronautical Science Foundation of China (No. 2018XXX).
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Xiang, X., Zhang, P., Yuan, Q., Li, R., Hu, R., Li, K. (2022). Few-Shot Learning Based on Convolutional Denoising Auto-encoder Relational Network. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_8
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