An operational concept capability requirement analysis method based on deep reinforcement learning

Jing An; Xuechao Zhang; Wei Liu; Wanting Rong

doi:10.1117/12.2671678

21 March 2023 An operational concept capability requirement analysis method based on deep reinforcement learning

Jing An, Xuechao Zhang, Wei Liu, Wanting Rong

Proceedings Volume 12609, International Conference on Computer Application and Information Security (ICCAIS 2022); 126091W (2023) https://doi.org/10.1117/12.2671678
Event: International Conference on Computer Application and Information Security (ICCAIS 2022), 2022, ONLINE, ONLINE

Abstract

Based on the perspective of combining qualitative analysis and quantitative calculation, a method of operational concept capability requirement analysis is designed based on deep reinforcement learning. Firstly, it obtains the simulation small sample data set with high reliability based on simulation experiment. Secondly, the operational concept surrogate model is constructed on the empirical data, and the surrogate model is optimized and trained by using the multi-objective optimization algorithm with high credibility simulation data set as input. Finally, the trained surrogate model is interacted with the deep reinforcement learning framework to realize the reverse exploration of operational concept capability requirements.

Citation Download Citation

Jing An, Xuechao Zhang, Wei Liu, and Wanting Rong "An operational concept capability requirement analysis method based on deep reinforcement learning", Proc. SPIE 12609, International Conference on Computer Application and Information Security (ICCAIS 2022), 126091W (21 March 2023); https://doi.org/10.1117/12.2671678

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available