摘要
提出了一种基于HAAR小波和BP神经的非线性电路故障诊断方法,该方法采用小波分解作为非线性电路故障信号的预处理器能大大减少神经网络的输入及训练和处理时间.介绍了一种改进的采用动量因子防止局部收敛的BPNN方法后,阐述基于HAAR小波分解提取故障信号中的故障特征的原理.
In this paper,a fault diagnosis method based on Haar wavelet and BP neural network for nonlinear analog circuit has been presented.The proposed method by using wavelet decomposition as a preprocessor of nonlinear analog circuit fault signal can drastically reduces the number of inputs and training and processing time of the neural network.After briefly introducing an improved BPNN which adpots momentum factor for preventing local convergency used for nonlinear analog-circuit fault diagnosis,the basic principle based on Haar wavelet decomposition that can extracts the fault feature from fault signal is discussed.
出处
《吉首大学学报（自然科学版）》
CAS
2003年第4期7-11,共5页
Journal of Jishou University(Natural Sciences Edition)
基金
SupportedbyNationalNaturalScienceFoundationofChina(5 0 2 770 10 )
