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| EMD遗传神经网络方法 | |
| 引用本文： | 许同乐,张新义,裴新才,贾庆轩. EMD遗传神经网络方法[J]. 北京邮电大学学报, 2012, 35(5): 68-72 |
| 作者姓名： | 许同乐 张新义 裴新才 贾庆轩 |
| 作者单位： | 北京邮电大学自动化学院,北京100876;山东理工大学机械工程学院,山东淄博255049;山东理工大学机械工程学院,山东淄博,255049;北京邮电大学自动化学院,北京,100876 |
| 基金项目： | 山东省高等学校科技计划项目(J10LG22) |
| 摘 要： | 针对BP(back propagation)神经网络搜索速度慢、容易陷入局部最小的缺陷,提出了经验模态分解(EMD)遗传神经网络方法,首先用对带噪的信号进行分解,得到信号的各阶本征模函数分量,每个本征模函数分量对应着一个能量不同的频段,即一种故障特征,将各频段能量的特征向量作为优化神经网络的输入样本;其次用遗传算法对神经网络的初始权值和阈值进行优化.利用EMD遗传神经网络方法对滚动轴承多类故障信号进行分析,可提高故障识别能力. |
| 关 键 词： | 经验模态 本征模函数 神经网络 遗传算法 |
| 收稿时间： | 2011-10-31 |
EMD Genetic Neural Networks Method |
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| XU Tong-le,ZHANG Xin-yi,PEI Xin-cai,JIA Qing-xuan. EMD Genetic Neural Networks Method[J]. Journal of Beijing University of Posts and Telecommunications, 2012, 35(5): 68-72 | |
| Authors: | XU Tong-le ZHANG Xin-yi PEI Xin-cai JIA Qing-xuan |
| Affiliation: | 1. School of Automation, Beijing University of Posts and Telecommunications China; 2. Mechanical Engineering School, Shandong University of Technology |
| Abstract: | To overcome intrinsic shortcomings of back propagation(BP)neural network, including slow convergence rate and easy trapping in local minimum, an empirical mode decomposition (EMD)genetic neural networks method is proposed. Firstly, EMD is used to decompose the signals with noise to obtain each intrinsic mode function, each intrinsic mode function corresponding to a frequency band with different energy or a fault feature, and feature vector of each frequency band is used as input sample to optimize neural network. Secondly, the genetic algorithm is used to optimize the weights and thresholds of BP neural network. This method is applied in a simulating experiment for the rolling bearings multiple fault signal analysis, and the ability of fault identification is therefore improved by this method. |
| Keywords: | empirical mode decomposition intrinsic mode function neural network genetic algorithm |
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