Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Changqiang Huang"'
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 1, Pp 847-868 (2023)
Abstract In this paper, a novel Adaptive Parameter Strategy Differential Evolution (APSDE) algorithm is proposed to overcome the parameters dependence and avoid local optima. The Parameter Update Mechanism (PUM), which has three different strategies,
Externí odkaz:
https://doaj.org/article/d10fb189070c4c2382ae55060a1c1f29
Publikováno v:
IEEE Access, Vol 11, Pp 73543-73555 (2023)
Reinforcement learning is an effective approach for solving decision-making problems. However, when using reinforcement learning to solve maneuver decision-making with sparse rewards, it costs too much time for training, and the final performance may
Externí odkaz:
https://doaj.org/article/e2d904be230b4be3bcbc7a20c6a1aaf7
Publikováno v:
Applied Sciences, Vol 13, Iss 16, p 9421 (2023)
Maneuver decision-making is essential for autonomous air combat. However, previous methods usually make decisions to aim at the target instead of hitting the target and use discrete action spaces instead of continuous action spaces. While these simpl
Externí odkaz:
https://doaj.org/article/dd27c58259e44fc6926cde85795ad5e6
Publikováno v:
Frontiers in Neurorobotics, Vol 16 (2022)
Autonomous maneuver decision-making methods for air combat often rely on human knowledge, such as advantage functions, objective functions, or dense rewards in reinforcement learning, which limits the decision-making ability of unmanned combat aerial
Externí odkaz:
https://doaj.org/article/8928eb0d596b4918872a192bcbba8c12
Autor:
Hongpeng Zhang, Changqiang Huang
Publikováno v:
IEEE Access, Vol 8, Pp 12976-12987 (2020)
Maneuver decision-making directly determines the success or failure of air combat. To improve the dogfight ability of unmanned combat aerial vehicles and avoid the deficiencies of traditional methods, such as poor flexibility and a weak decision-maki
Externí odkaz:
https://doaj.org/article/b0915092b018416aa33a9658c96fc17b
Publikováno v:
Applied Sciences, Vol 12, Iss 20, p 10230 (2022)
Maneuver decision-making is the core of autonomous air combat, and reinforcement learning is a potential and ideal approach for addressing decision-making problems. However, when reinforcement learning is used for maneuver decision-making for autonom
Externí odkaz:
https://doaj.org/article/9ec0f158c64c4b50bd1892c8dbd16028
Publikováno v:
IEEE Access, Vol 7, Pp 57795-57804 (2019)
To overcome the IMM algorithm is easy divergence and low tracking accuracy when dealing with complex maneuvering situations, this paper proposes an improved interactive multiple model strong tracking square room cubature Kalman filter (IIMM-STSRCKF)
Externí odkaz:
https://doaj.org/article/fa57ba84f693431fa4c34cfd19b26e56
Publikováno v:
IEEE Access, Vol 7, Pp 66084-66109 (2019)
Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NRO) is prese
Externí odkaz:
https://doaj.org/article/6ce88a7edc1049658907b9452183a453
Publikováno v:
Materials, Vol 14, Iss 20, p 6206 (2021)
In this work, quasistatic mechanical compression experiments were used to study the stress–strain relationship of aluminum foam, and the mechanism of the compressive deformation of aluminum foam under quasistatic compression conditions is discussed
Externí odkaz:
https://doaj.org/article/f785357558d9461a90c4fc3afb598256
Publikováno v:
Advances in Mechanical Engineering, Vol 8 (2016)
This article investigates the problem of designing a novel maneuvering decision-making method for the unmanned combat aerial vehicle. The design objective is to promote the real-time ability of decision-making method and solve the problem of uncertai
Externí odkaz:
https://doaj.org/article/9329132eb5b9400c998c1b3a5e7d9b93