基于特征参数的任务可用能力评估

梁晓玲; 邓建辉; 陈思均; 庄德宇

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

基于特征参数的任务可用能力评估

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
4.
大连海事大学航海学院, 辽宁大连, 116026

基金项目: 国防科技基础加强计划项目资助(2019-JCJQ-ZD-XXX-00); 国家自然科学基金青年基金(52301417); 中央基本科研业务费(3132024203); 国家自然科学基金重大科研仪器研制项目(42327901).

详细信息

作者简介:
梁晓玲(1985-), 女, 博士, 副教授, 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; TJ630
计量
- 文章访问数: 89
- HTML全文浏览量: 53
- PDF下载量: 71
- 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

Task Availability Capability Assessment Based onCharacteristic Parameters

1.
College of Marine Engineering, Dalian Maritime University, Dalian 116026, China
2.
Unit 92942^th, the Liberation Army of China, Beijing 100161, China
3.
Unit 92557^th, the Liberation Army of China, Guangzhou 510720, China
4.
College of Navigation, Dalian Maritime University, Dalian 116026, China

摘要

摘要: 针对实时故障特征参数输入情况下评估系统任务能力的问题, 文中提出了一种基于特征参数的任务可用能力评估方法。通过引入神经模糊系统(NFS)建立航保系统易损性模型, 将模糊规则融入神经网络框架中, 建立了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, 创建了真实系统的替代模型, 具有更高的普适性。利用智能优化算法使替代模型尽可能接近真实模型, 摆脱了系统中未知权重系数依赖于专家或经验的限制, 为模糊神经网络(FNN)赋予了学习能力。实验结果和分析表明该评估模型全面而合理, 该方法可以扩展应用于水下领域, 评估航保系统和舰船系统的任务能力。
- 粒子群优化算法 /
- 模糊神经网络 /
- 任务可用能力评估
Abstract: To evaluate system task availability under the condition of real-time fault feature parameter input, a method of assessing task availability capability based on characteristic parameters was proposed. By introducing the neuro-fuzzy system(NFS), a vulnerability model of the aviation insurance system was established, and fuzzy rules were integrated into the framework of neural networks to establish an assessment model for system task availability capability. This method combined the reasoning ability of fuzzy logic and the infinite approximation function ability of neural networks to create an alternative model of the real system, which was more universal. Moreover, an intelligent optimization algorithm was used to make the alternative model approach to the real model, getting rid of the influence of unknown weight coefficients in the system that relied on experts or experience and endowing the fuzzy neural network(FNN) with learning capabilities. The experimental results and analysis show that the assessment model is comprehensive and reasonable and can be extended to the underwater field to assess the mission capabilities of navigation support systems and naval ship systems.
- particle swarm optimization algorithm /
- fuzzy neural network /
- task availability capability assessment

HTML全文

图 1 方法设计流程

Figure 1. Flow chart of method design

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图 2 航保系统易损性逻辑结构

Figure 2. Logical structure 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 适应度函数值随学习次数变化曲线

Figure 4. Change of fitness function value with iterations

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图 5 损伤概率对易损性概率的影响

Figure 5. Effect of damage probability on the probability of vulnerability

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图 6 智能优化算法在预测各级损伤概率时的收敛过程

Figure 6. Convergence process of intelligent optimization algorithm in predicting damage probabilities

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图 7 基于NFS模型的测试集性能对比

Figure 7. Performance comparison chart of NFS model on test set

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