Journal of System Simulation
Abstract
Abstract: To effectively cope with the dimension curse in simulation testing and reduce the number of simulations times needed in the traditional full-space parameter traversal, it is necessary to obtain specific simulation data to accurately reflect the modeling characteristics of the test data to obtain the informative and representative samples of the original data with a smaller number of simulations. A digital simulation test model for adaptive recognition ;/of capability boundary parameters for UAS is proposed. The model is initially constructed with a good point set with a multi-weight structure; In combination with an adaptive kernel function boundary point recognition, the model is iteratively optimized by Gaussian process regression, so as to adaptively detect the capability boundary of UAS. The experimental results show that the method can reduce the amount of data required for modeling and improve the efficiency of adaptive parameter boundary recognition, which provides an approach to enhance the efficiency of intelligent UAS testing.
Recommended Citation
Li, Jinwen; Wang, Peng; Pan, Youmei; and Hui, Xinyao
(2024)
"Adaptive Recognition Method of Capability Boundary Parameters for Unmanned
Autonomous Systems,"
Journal of System Simulation: Vol. 36:
Iss.
10, Article 12.
DOI: 10.16182/j.issn1004731x.joss.23-0775
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss10/12
First Page
2359
Last Page
2370
CLC
TP391.9
Recommended Citation
Li Jinwen, Wang Peng, Pan Youmei, et al. Adaptive Recognition Method of Capability Boundary Parameters for Unmanned Autonomous Systems[J]. Journal of System Simulation, 2024, 36(10): 2359-2370.
DOI
10.16182/j.issn1004731x.joss.23-0775
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons