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: 77
- HTML全文浏览量: 47
- PDF下载量: 62
- 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

下载: 全尺寸图片幻灯片

图 2 航保系统易损性逻辑结构

Figure 2. Logical structure of aviation security system vulnerability

下载: 全尺寸图片幻灯片

图 3 优化结果与理想值对比

Figure 3. Comparison between optimization results and ideal values

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

参考文献(26)

[1]	孙诗南. 现代航空母舰[M]. 上海: 上海科学普及出版社, 2000: 84-85.
[2]	Wasmund T L. New model to evaluate weapon effects and platform vulnerability: AJEM[J]. Wstiac Newsletter, 2001, 2: 1-3.
[3]	PUSEY H C. Technical information support for survivability[J]. The Shock and Vibration, 1983, 53(1): 21-31.
[4]	谢宗仁, 吕建伟, 徐一帆, 等. 大型武器装备总体战备完好性的多层次协调优化[J]. 系统工程与电子技术, 2017, 39(12): 2729-2737. doi: 10.3969/j.issn.1001-506X.2017.12.15 XIE Z R, Lü J W, XU Y F, et al. Multi-level coordinated optimization of major weapon equipment’s overall operational readiness indicators[J]. Systems Engineering and Electronics, 2017, 39(12): 2729-2737. doi: 10.3969/j.issn.1001-506X.2017.12.15
[5]	程莉莉, 罗威, 胡芷毅, 等. 舰载作战系统战备状态评价方法研究[J]. 计算机与数字工程, 2017, 45(9): 1790-1794. doi: 10.3969/j.issn.1672-9722.2017.09.021 CHENG L L, LUO W, HU Z Y, et al. Research on evaluation of the ship-borne combat system operational states[J]. Computer & Digital Engineering, 2017, 45(9): 1790-1794. doi: 10.3969/j.issn.1672-9722.2017.09.021
[6]	彭辉, 姜强, 邓建辉, 等. 基于云模型的舰船战备完好性评估方法研究[J]. 中国舰船研究, 2021, 16(6): 61-71. PENG H, JIANG Q, DENG J H, et al. Warship operational readiness integrity evaluation method based on cloud model[J]. Journal of Ship Research, 2021, 16(6): 61-71.
[7]	KORIEM S M. A fuzzy Petri net tool for modeling and verification of knowledge-based systems[J]. The Computer Journal, 2000, 43(3): 206-223. doi: 10.1093/comjnl/43.3.206
[8]	MAGOTT J, SKROBANEK P. Method of time Petri net analysis for analysis of fault trees with time dependencies[J]. IEEE Proceedings Computers and Digital Techniques, 2002, 149(6): 257-271. doi: 10.1049/ip-cdt:20020804
[9]	LI X O, YU W, FELIPE L R. Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework[J]. IEEE Transactions on Systems, Man & Cybernetics: Part C-Applications & Reviews, 2000, 30(4): 442-449.
[10]	LI X O, FELIPE L R. Adaptive fuzzy Petri nets for dynamic knowledge representation and inference[J]. Expert Systems with Applications, 2000, 19(3): 235-241. doi: 10.1016/S0957-4174(00)00036-1
[11]	夏世芬, 毛大会, 徐杨. 一种算子模糊逻辑系统及其Petri网推理算法[J]. 模糊系统与数学, 2008, 22(1): 7-14.
[12]	SHI Y, EBERHART R C. A modified particle swarm optimizer[C]//IEEE International Conference of Evolutionary Computation. Alaska: IEEE, 1998: 69-73.
[13]	CLERC M. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization[C]//Proceedings of the 1999 Congress on Evolutionary Computation. Washington, USA: IEEE, 1999: 1951-1957.
[14]	SCHOUTEN N J, SALMAN M A, KHEIR N A. Fuzzy logic control for parallel hybrid vehicles[J]. IEEE Trans on Control Systems Technology, 2002, 10(3): 460-468. doi: 10.1109/87.998036
[15]	MENDEL J M. Type-2 fuzzy sets and systems: An overview[J]. IEEE Computational Intelligence Magazine, 2007, 2(1): 20-29. doi: 10.1109/MCI.2007.380672
[16]	MENDEL J M. Advances in type-2 fuzzy sets and systems[J]. Information Sciences, 2007, 177(1): 84-110. doi: 10.1016/j.ins.2006.05.003
[17]	李典庆, 张圣坤. 水面舰船生命力研究现状及方向概述[J]. 造船技术, 2003(5): 3-6, 10. doi: 10.3969/j.issn.1000-3878.2003.05.001
[18]	SHU M H, CHENG C H, CHANG J R. Using intuitionistic fuzzy setsfor fault-tree analysis on printed circuit board assembly[J]. Microelectronics Reliability, 2006, 46: 2139-2148. doi: 10.1016/j.microrel.2006.01.007
[19]	ISAZADEH A, NOURI H, REZVAN M. An evolutionary system architecture for information protection[J]. Applied Mathematics and Computation, 2008, 197: 687-691. doi: 10.1016/j.amc.2007.08.038
[20]	DONG Y H, YU D T. Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis[J]. Journal of Loss Prevention in the Process Industries, 2005, 18(2): 83-88.
[21]	OLARU C, WEHENKEL L. A complete fuzzy decision tree technique[J]. Fuzzy Sets and Systems, 2003, 138(2): 221-254. doi: 10.1016/S0165-0114(03)00089-7
[22]	DENG W, LIU H L, XU J J, et al. An improved quantum-inspired differential evolution algorithm for deep belief network[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(10): 7319-7327. doi: 10.1109/TIM.2020.2983233
[23]	TALPUR N, ABDULKADIR S J, ALHUSSIAN H, et al. Deep neuro-fuzzy system application trends, challenges, and future perspectives: A systematic survey[J]. Artificial intelligence review, 2023, 56(2): 865-913. doi: 10.1007/s10462-022-10188-3
[24]	SHIHABUDHEEN K V, PILLAI G N. Recent advances in neuro-fuzzy system: A survey[J]. Knowledge-Based Systems, 2018, 152: 136-162. doi: 10.1016/j.knosys.2018.04.014
[25]	TAN H, REN Z Y, YAN W, et al. A wind power accommodation capability assessment method for multi-energy microgrids[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2482-2492. doi: 10.1109/TSTE.2021.3103910
[26]	HOSSEINI S A, ABBASZADEH S A, ASHEGHI R. Prediction of bedload transport rate using a block combined network structure[J]. Hydrological Sciences Journal, 2022, 67(1): 117-128. doi: 10.1080/02626667.2021.2003367