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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

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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

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