基于小波神经网络的振动速度传感器幅频特性补偿研究

Research on the amplitude frequency characteristics compensation based on wavelet neural network for vibration velocity transducer

为了实现超低频振动速度测量,提出补偿其幅频特性的小波神经网络方法.该方法以振动速度传感器动态实验数据为基础,通过小波神经网络训练来确定传感器幅频特性补偿网络.介绍振动速度传感器幅频特性补偿原理,分析网络的拓扑结构,给出网络参数训练和初始化方法.采用引入动量项的最速下降法训练网络权值、尺度因子和平移因子,将小波网络参数的初始化与小波类型、小波时频参数和学习样本等联系起来.结果表明,采用小波神经网络进行振动速度传感器幅频特性补偿具有良好的鲁棒性,并能实现在线补偿,网络训练的速度和精度优于同等规模的BP网络,在测试领域有重要的实用价值.

A method of amplitude frequency characteristics compensation is presented to realize ultra-low frequency vibration measurement based on wavelet neural network（WNN） for vibration velocity transducer. In this method, a dynamic compensation network can be set up according to measurement data of dynamic response of vibration velocity transducer. The compensation principle is introduced and the geometrical structure of the network is analyzed and the algorithms for the training and initialization of network parameters are given. The weights of network, scale factor and displacement factor are trained by the steepest descent method and the network parameters initialization is integrated with the wavelet type, time-frequency parameters of wavelet and the training samples. The results show that the proposed wavelet neural network has good robustness, on-line correction ability, and higher precision and faster training speed than the BP neural network when used in the amplitude frequency characteristics compensation of vibration velocity transducer, and has practical value in measurement field.