| Autor: |
AN Jing, SI Guangya, YAN Jiang |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Zhihui kongzhi yu fangzhen, Vol 45, Iss 5, Pp 1-9 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1673-3819 |
| DOI: |
10.3969/j.issn.1673-3819.2023.05.001 |
| Popis: |
Capability requirement analysis is the key stage of operational concept development. Based on the formal description of the operational concept capability requirement analysis, a method of operational concept capability requirement analysis based on DRL(deep reinforcement learning) is designed from the perspective of qualitative and quantitative combination. In this method, small sample data sets with high reliability can be obtained through simulation experiments. Based on the experience data, the surrogate model of operation concept is constructed, and the model is optimized and trained by using multi-objective optimization algorithm with the high credibility simulation data set as the input. Finally, the agent model obtained from the training and the DRL framework are interactively optimized to achieve the reverse exploration of the operational concept capability requirements. The experiment results show that the method is feasible. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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