基于特征参数的任务可用能力构建与优化

梁晓玲; 邓建辉; 陈思均

doi:10.11993/j.issn.2096-3920.2023-0126

基于特征参数的任务可用能力构建与优化

doi: 10.11993/j.issn.2096-3920.2023-0126

1.
大连海事大学轮机工程学院, 辽宁大连, 116026
2.
中国人民解放军92942部队, 北京, 100161
3.
中国人民解放军92557部队, 广东广州, 510720

基金项目: 国防科技基础加强计划项目资助(2019-JCJQ-ZD-XXX-00)

详细信息

作者简介:
梁晓玲(1985-), 女, 博士, 讲师, 主要研究方向为船舶制导与控制, 可靠性分析

中图分类号: U662; TJ6
计量
- 文章访问数: 9
- HTML全文浏览量: 1
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-14
- 修回日期: 2023-12-18
- 录用日期: 2024-01-05
- 网络出版日期: 2024-02-07

System Capability Assessment Modeling Based on Characteristic Parameters

1.
College of Marine Engineering, Dalian Maritime University, Dalian 116026, China
2.
Chinese People's Liberation Army Unit 92942, Beijing, 100161, China
3.
Chinese People's Liberation Army Unit 92557, Guangzhou, 510720, China

摘要

摘要: 文中提出了神经模糊系统(NFS)建立航保系统的易损性模型, 将模糊规则引入神经网络的框架中, 建立系统任务可用能力评估模型。该方法结合了模糊逻辑的推理能力和神经网络的无限逼近函数能力, 建立真实系统的替代模型, 更具普适性。采用智能优化算法使得替代模型最大限度接近真实模型, 摆脱了系统中未知权重系数依赖于专家或经验的影响, 赋予模糊神经网络(FNN)学习能力。实验结果和分析表明评估的全面性和合理性模型, 可应用于航保系统能力评估研究。
- 粒子群优化算法 /
- 模糊神经网络 /
- 任务可用能力评估
Abstract: This paper proposes a neuro-fuzzy system to establish a vulnerability model of the aviation insurance system, introduces fuzzy rules into the framework of the neural network, and establishes an evaluation model for the system's task capabilities. This method combines the reasoning ability of fuzzy logic and the infinite approximation function ability of neural networks to establish an alternative model of the real system, which is more universal. Secondly, an intelligent optimization algorithm is used to make the alternative model as close as possible to the real model, getting rid of the influence of the unknown weight coefficient in the system that relies on experts or experience, and endows the neural fuzzy network with learning capabilities. The experimental results and analysis show that the assessment model is comprehensive and reasonable and can be applied to aviation insurance system capability assessment research.
- particle swarm optimization algorithm /
- fuzzy neural network /
- system capability assessment

HTML全文

图 1 方法设计流程

Figure 1. The flowchart of design method

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图 2 航保系统易损性的逻辑结构示意图

Figure 2. Logical structure diagram of aviation security system vulnerability

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图 3 优化结果与理想值对比

Figure 3. Comparison between optimization results and ideal values

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图 4 适应度函数值变化

Figure 4. Fitness function value changes

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图 5 P₁, P₂变化对易损性概率的影响

Figure 5. The impact of changes in P₁ and P₂ on vulnerability probability

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图 6 P₂, P₃变化对易损性概率的影响

Figure 6. The impact of changes in P₂ and P₃ on vulnerability probability

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图 7 P₃, P₄变化对易损性概率的影响

Figure 7. The impact of changes in P₃ and P₄ on vulnerability probability

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图 8 P₄, P₅变化对易损性概率的影响

Figure 8. The impact of changes in P₄ and P₅ on vulnerability probability

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图 9 智能优化算法在预测各级损伤概率时的收敛过程

Figure 9. The convergence process of intelligent optimization algorithms in predicting damage probabilities

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图 10 NFS模型在测试集上的性能对比图

Figure 10. Performance comparison chart of NFS model on test set

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