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关于系统级故障诊断的烟花-反向传播神经网络算法

归伟夏 陆倩 苏美力

归伟夏, 陆倩, 苏美力. 关于系统级故障诊断的烟花-反向传播神经网络算法[J]. 电子与信息学报, 2020, 42(5): 1102-1109. doi: 10.11999/JEIT190484
引用本文: 归伟夏, 陆倩, 苏美力. 关于系统级故障诊断的烟花-反向传播神经网络算法[J]. 电子与信息学报, 2020, 42(5): 1102-1109. doi: 10.11999/JEIT190484
Weixia GUI, Qian LU, Meili SU. A Firewoks Algorithm-Back Propagation Fault Diagnosis Algorithm for System-level Fault Diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1102-1109. doi: 10.11999/JEIT190484
Citation: Weixia GUI, Qian LU, Meili SU. A Firewoks Algorithm-Back Propagation Fault Diagnosis Algorithm for System-level Fault Diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1102-1109. doi: 10.11999/JEIT190484

关于系统级故障诊断的烟花-反向传播神经网络算法

doi: 10.11999/JEIT190484
基金项目: 国家自然科学基金(61862003, 61862004),广西研究生教育创新计划资助项目(YCSW2019036)
详细信息
    作者简介:

    归伟夏:女,1974年生,副教授,博士,研究方向为智能计算、网络与并行分布式计算

    陆倩:女,1994年生,硕士生,研究方向为智能算法、并行计算

    苏美力:女,1989年生,硕士生,研究方向为智能算法、并行计算

    通讯作者:

    陆倩 563766390@qq.com

  • 中图分类号: TP306

A Firewoks Algorithm-Back Propagation Fault Diagnosis Algorithm for System-level Fault Diagnosis

Funds: The National Natural Science Foundation of China (61862003, 61862004), The Innovation Project of Guangxi Graduate Education (YCSW2019036)
  • 摘要:

    为了更快速且精确地诊断出大规模多处理器系统中的故障单元,该文首次将改进的烟花算法和反向传播(BP)神经网络相结合,提出一种新的系统级故障诊断算法—烟花-反向传播神经网络故障诊断算法(FWA-BPFD)。首先,在烟花算法中引入双种群策略、协作算子以及最优算子,设计新的适应度函数,优化变异算子、映射规则和选择策略。然后,利用烟花算法全局搜索能力和局部搜索能力的自调节机制,优化BP神经网络中的权值和阈值的寻优过程。仿真实验结果表明,该文算法相较于其他算法不仅有效地降低了迭代次数和训练时间,而且还进一步提高了诊断精度。

  • 图  1  3层BP神经网络结构图

    图  2  均匀交叉运算示意图

    图  3  烟花算法优化BP神经网络的流程图

    图  4  各关键参数对算法CPU运行时间的影响

    图  5  算法训练性能图

    图  6  不同系统规模中4种算法诊断正确率的比较

    表  1  PMC诊断模型

    测试结点${u_i}$被测试结点${u_j}$测试结果${u_{ij}}$
    000
    011
    100/1
    110/1
    下载: 导出CSV

    表  2  烟花算法的其它参数设置

    参数名称参数说明参数值
    ${A_{\rm{min}}}$烟花的最小爆炸半径2
    ${p_{\rm{c}}}$协作算子交叉概率0.5
    ${X_{\rm{LB}}}$烟花位置下界值0
    ${X_{\rm{UB}}}$烟花位置上界值1
    T最大迭代次数1000
    下载: 导出CSV

    表  3  神经网络训练关键参数设置

    参数名称参数说明参数值
    show设置数据显示刷新频率30
    lr网络的学习率0.01
    goal网络输出误差最小值7e-07
    epochs最大迭代次数10000
    下载: 导出CSV

    表  4  4种算法在不同系统规模中的性能比较

    算法名称$n = 50$$n = 100$
    训练时间(s)迭代次数训练时间(s)迭代次数
    BPFD412685341635937
    CS-BPFD233327178102134
    GA-BPFD310365278903978
    本文FWA-BPFD212305167551998
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-28
  • 修回日期:  2020-01-19
  • 网络出版日期:  2020-02-13
  • 刊出日期:  2020-06-04

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