摘要
This paper proposes an ensemble transfer learning guidance algorithm for angular-constrained midcourse guidance to maximize the terminal velocity. The algorithm developed improves the generalization capability of the trained deep neural network to adapt to a new environment. First several deep neural guidance networks are trained for some specific working environments via supervised learning. A small-scale ensemble transfer learning network is then leveraged to fuse the knowledge of different pretrained deep neural network. This requires much less labeled data to transfer existing knowledge to a new working environment and hence greatly improves the learning efficiency, compared to the supervised learning philosophy. Extensive numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm.
源语言 | 英语 |
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页(从-至) | 204-215 |
页数 | 12 |
期刊 | Journal of Aerospace Information Systems |
卷 | 20 |
期 | 4 |
DOI | |
出版状态 | 已出版 - 1 4月 2023 |