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基于PSO改进RBF神经网络的威胁评估方法研究
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Abstract:
导弹威胁评估是飞机对抗威胁过程中的一个重要环节，是干扰决策的前提。本文介绍了神经网络在军事领域的应用，接着通过粒子群算法优化RBF神经网络，利用优化后的网络解决导弹威胁评估问题，提出了基于粒子群优化RBF神经网络的威胁评估方法，使用粒子群对RBF神经网络参数寻优。用此方法与BP神经网络、RBF神经网络算法性能进行比较，结果表明此方法更有优势，能够快速、准确地评估威胁。
Missile threat assessment is an important link in the process of aircraft countering threats and is a prerequisite for interference decision-making. This paper introduces the application of neural net-work in the military field, and then optimizes RBF neural network through particle swarm optimization algorithm. The optimized network is used to solve the problem of missile threat assessment. A threat assessment method based on Particle swarm optimization RBF neural network is proposed, which uses particle swarm optimization to optimize the parameters of RBF neural network. Comparing the performance of this method with BP neural network and RBF neural network algorithms, the results show that this method has more advantages and can quickly and accurately evaluate threats.
| [1] | 黄骏, 李永宾, 温玉涛. 基于GA-AHP算法的多目标威胁评估[J]. 空军工程大学学报(自然科学版), 2019, 20(5): 90-96. |
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| [5] | Wu, Z., Hu, S., Luo, Y., et al. (2022) Optimal Distributed Cooperative Jamming Resource Allocation for Multi-Missile Threat Scenario. IET Radar, Sonar & Navigation, 16, 113-128. https://doi.org/10.1049/rsn2.12168 |
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| [8] | Shi, Q.U. and Liu, J.X. (2019) Improved Grey Correlation Analysis for Ballistic Missile Threat Assessment. 2019 6th International Conference on Information Science and Control Engi-neering (ICISCE), Shanghai, 20-22 December 2019, 338-342. https://doi.org/10.1109/ICISCE48695.2019.00074 |
| [9] | 戴少怀, 杨革文, 郁文, 等. 基于RBF神经网络的雷达有源压制干扰识别[J]. 空天防御, 2022, 5(1): 102-107. |
| [10] | Ramírez-Ochoa, D.D., Pérez-Domínguez, L.A., Mar-tínez-Gómez, E.A., et al. (2022) PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strat-egy: A Review. Symmetry, 14, 455.
https://doi.org/10.3390/sym14030455 |
| [11] | 黄骏, 李永宾, 温玉涛. 基于GA-AHP算法的多目标威胁评估[J]. 空军工程大学学报(自然科学版), 2019, 20(5): 90-96. |
| [12] | 吕陆琴. 面向态势感知的多源数据融合分析研究[D]: [硕士学位论文]. 北京: 北京邮电大学, 2021. |
| [13] | 李威, 卢盈齐. 基于聚类组合赋权的空袭目标威胁评估方法[J]. 现代防御技术, 2022, 50(3): 17-24. |
| [14] | 吴琼. 基于改进CNN的雷达辐射源识别算法研究[D]: [硕士学位论文]. 西安: 西安电子科技大学, 2019. |
| [15] | Wu, Z., Hu, S., Luo, Y., et al. (2022) Optimal Distributed Cooperative Jamming Resource Allocation for Multi-Missile Threat Scenario. IET Radar, Sonar & Navigation, 16, 113-128. https://doi.org/10.1049/rsn2.12168 |
| [16] | 何亮亮. 基于蚁群算法的RBF神经网络算法研究[D]: [硕士学位论文]. 西安: 西安工程大学, 2019. |
| [17] | Qu, S., Xu, T., Ma, L., et al. (2019) An Improved VIKOR Model for Ballistic Mis-sile Threat Assessment and Ranking. 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019), Xiamen, 27-28 October 2019, 34-38. https://doi.org/10.2991/mmsta-19.2019.8 |
| [18] | Shi, Q.U. and Liu, J.X. (2019) Improved Grey Correlation Analysis for Ballistic Missile Threat Assessment. 2019 6th International Conference on Information Science and Control Engi-neering (ICISCE), Shanghai, 20-22 December 2019, 338-342. https://doi.org/10.1109/ICISCE48695.2019.00074 |
| [19] | 戴少怀, 杨革文, 郁文, 等. 基于RBF神经网络的雷达有源压制干扰识别[J]. 空天防御, 2022, 5(1): 102-107. |
| [20] | Ramírez-Ochoa, D.D., Pérez-Domínguez, L.A., Mar-tínez-Gómez, E.A., et al. (2022) PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strat-egy: A Review. Symmetry, 14, 455.
https://doi.org/10.3390/sym14030455 |
