• 中国科技核心期刊
  • JST收录期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

梁晓玲, 邓建辉, 陈思均. 基于特征参数的任务可用能力构建与优化[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.2023-0126
引用本文: 梁晓玲, 邓建辉, 陈思均. 基于特征参数的任务可用能力构建与优化[J]. 水下无人系统学报, xxxx, x(x): x-xx doi: 10.11993/j.issn.2096-3920.2023-0126
LIANG Xiaoling, DENG Jianhui, CHEN Sijun. System Capability Assessment Modeling Based on Characteristic Parameters[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0126
Citation: LIANG Xiaoling, DENG Jianhui, CHEN Sijun. System Capability Assessment Modeling Based on Characteristic Parameters[J]. Journal of Unmanned Undersea Systems. 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
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

System Capability Assessment Modeling Based on Characteristic Parameters

  • 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。

     

  • 图  1  方法设计流程

    Figure  1.  The flowchart of design method

    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

    图  3  优化结果与理想值对比

    Figure  3.  Comparison between optimization results and ideal values

    图  4  适应度函数值变化

    Figure  4.  Fitness function value changes

    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

    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

    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

    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

    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

    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

  • [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 Zongren, Lü Jianwei, Xu Yifan, 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 Lili, Lu Wei, Hu Zhiyi, 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 Hui, Jiang Qiang, Deng Jianhui, et al. Warship operational readiness integrity evaluation method based on cloud modelChinese[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] Xia S F, Mao D H, Xu Y. An operator fuzzy logic system and its inference algorithm of Petri net[J]. Fuzzy Systems and Mathmetics, 2008, 22(1): 7-14.
    [13] Shi Y, Eberhart R C. A modified particle swarm optimizer[C]//IEEE International Conference of Evolutionary Computation, Alaska: IEEE, 1998: 69-73.
    [14] 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.
    [15] 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
    [16] 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
    [17] 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
    [18] 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
    [19] 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
    [20] 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
    [21] 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.
    [22] 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
    [23] 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
    [24] 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
    [25] 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
    [26] 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
    [27] Hosseini S A, Abbaszadeh Shahri 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
  • 加载中
图(10)
计量
  • 文章访问数:  28
  • HTML全文浏览量:  15
  • PDF下载量:  3
  • 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

目录

    /

    返回文章
    返回
    服务号
    订阅号