基于小波变换和反向传播网络的模态参数辨识

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振动与冲击 ›› 2012, Vol. 31 ›› Issue (3) : 55-59.

论文

基于小波变换和反向传播网络的模态参数辨识

代煜；张建勋

作者信息 +

Identification of modal parameters based on wavelet transform and back propagation network

DAI Yu； ZHANG Jian-xun

Author information +

文章历史 +

摘要

为了减小连续小波变换带来的边界效应对模态参数识别的影响，提出利用人工神经网络对自由衰减响应信号进行双向延拓。设计了多输入单输出的反向传播网络，网络根据当前有限多个离散
采样点数据预测下一时刻信号的幅值，训练网络的样本来自于对原始信号的简单分组。使用数值仿
真和实验检验了提出方法的实用性。实验装置是用于微创外科手术机器人的力传感器。结果表明提
出的方法能够准确地从短信号中辨识出阻尼比和无阻尼自振频率。

Abstract

In order to reduce the influence of edge-effect of continuous wavelet transform on the
identification of modal parameters, artificial neural network is introduced to extend free decay response
signal in both directions. A multiple-input and single-output back propagation network is devised to predict
the signal at the next time step using a finite number of current samples. The training samples are obtained
by simple combination of the original signal. Both numerical and experimental studies are performed to
demonstrate the proposed procedure and verify its practicability. The experimental system is a force sensor
developed for minimally invasive surgery robot. The results show the method works very well in
identifying damping ratios and undamped natural frequencies from short signal.

导出引用

代煜;张建勋. 基于小波变换和反向传播网络的模态参数辨识[J]. 振动与冲击, 2012, 31(3): 55-59

DAI Yu;ZHANG Jian-xun. Identification of modal parameters based on wavelet transform and back propagation network[J]. Journal of Vibration and Shock, 2012, 31(3): 55-59