基于小波神经网络采煤机液压传感器非线性误差的研究  

Nonlinear Error Compensation of Shearer Hydraulic Sensors Based on Wavelet Neural Networks

作　　者：孙月华[1] 李春华[1] 吴卫东[1] 

机构地区：[1]黑龙江科技学院机械工程分院,黑龙江哈尔滨150000

出　　处：《矿山机械》2008年第23期11-13,共3页Mining & Processing Equipment

基　　金：黑龙江省教育厅2008年度科学技术研究项目(11533058)

摘　　要：研究了一种用小波神经网络对采煤机工况监测系统液压传感器非线性误差补偿的方法,给出了相应的算法和计算公式。该方法可将传感器传输特性的非线性模型,改造成为与实际物理过程相一致的不失真的线性模型,从而使传感器的非线性误差大大减少,应用实例结果表明了该方法的有效性。The compensative method of nonlinear error of hydraulic sensors used in the shearer working state monitoring system is studied by the wavelet neural networks, and the corresponding algorithm and formulas are presented. With this method, the nonlinear model of the sensor can be changed into a non-distortion linear model that is consistent with the actual physical process, so the nonlinear error of sensor reduces greatly. The example results show that this method is effective.

关 键 词：液压传感器 采煤机 液压系统 非线性误差 补偿 小波神经网络 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]