A Firewoks Algorithm-Back Propagation Fault Diagnosis Algorithm for System-level Fault Diagnosis
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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。
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关键词:
- 系统级故障诊断 /
- 烟花算法 /
- 反向传播神经网络 /
- PMC模型 /
- 烟花-反向传播神经网络算法
Abstract:In order to diagnose fault units in the large-scale multiprocessor systems more quickly and accurately, a system-level fault diagnosis algorithm—FireWorks Algorithm-Back Propagation Fault Diagnosis (FWA-BPFD) based on fireworks algorithm and Back Propagation(BP) neural network is proposed. Firstly, two population strategy, cooperative operator and optimal operator are introduced into fireworks algorithm. A new fitness function is designed, and the mutation operator, mapping rule and selection strategy are optimized. Then, the optimization process of weight and threshold value in BP neural network is optimized by the self-regulating mechanism of global and local searching ability of fireworks algorithm. Simulation results show that compared with other algorithms, this algorithm not only reduces the number of iterations and training time, but also improves the accuracy of diagnosis.
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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 
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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
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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 BPFD 412 685 34163 5937 CS-BPFD 233 327 17810 2134 GA-BPFD 310 365 27890 3978 本文FWA-BPFD 212 305 16755 1998
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