| AFSA＋PSO混合算法在BP网络故障诊断中的应用 |
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| 引用本文： | 王敏,周树道,段黎明,白衡. AFSA＋PSO混合算法在BP网络故障诊断中的应用[J]. 自动化技术与应用, 2014, 0(3): 62-65 |
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| 作者姓名： | 王敏 周树道 段黎明 白衡 |
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| 作者单位： | 解放军理工大学气象海洋学院,江苏南京211101 |
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| 基金项目： | 无人机全景万象技术研究（编号41301370） |
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| 摘 要： | 提出一种基于人工鱼群算法和粒子群算法混合训练BP网络的故障诊断系统.采用人工鱼群算法和粒子群算法结合算法训练神经网络权值,局部搜索速度快且保证全局收敛,有效克服了传统的BP神经网络收敛速度慢且容易陷入局部极值的缺点.将该网络用于齿轮箱故障诊断,并与传统BP模型用于故障诊断结果进行了比较,取得了较好的效果.
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| 关 键 词： | 人工鱼群算法 粒子群算法 BP神经网络 故障诊断 |
Application of AFSA and PSO in BP Network Fault Diagnosis |
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| WANG Min,ZHOU Shu-dao,DUAN Li-ming,BAI Heng. Application of AFSA and PSO in BP Network Fault Diagnosis[J]. Techniques of Automation and Applications, 2014, 0(3): 62-65 |
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| Authors: | WANG Min ZHOU Shu-dao DUAN Li-ming BAI Heng |
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| Affiliation: | ( Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 China ) |
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| Abstract: | A BP neural network fault diagnosis system is proposed based on artificial fish-swarm algorithm （AFSA） and particle swarm optimization （PSO） algorithm. AFSA combines with PSO, and they optimize the weight of BP network together. The mixed algorithm has a fast searching speed and is convergent global, overcomes the drawbacks of the traditional BP neural network. The system is used to solve the fault diagnosis problem of the gear box. The new system compared with traditional BP network, the better effect is obtained. |
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| Keywords: | artificial fish-swarm algorithm particle swarm optimization BP neural network fault diagnosis |
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