利用神经网络提高编码器精度的方法  

Improvement of precision of encoder based on neural network

作　　者：续志军[1] 洪喜[1] 于欣[1] 

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033

出　　处：《中国光学与应用光学》2008年第1期62-65,共4页Chinese Optics and Applied Optics Abstracts

摘　　要：介绍了编码器误差的构成及特点,针对系统误差的分布规律与特点提出了基于神经网络的误差修正方法。采用非线性逼近精度较高的径向基函数神经网络,以采样点的角度值作为网络的输入样本,以高精度检测编码器的检测值作为学习目标建立了误差修正模型。实验结果表明,采用此种方法可将编码器的精度提高至原来的3倍以上,可有效地改善编码器的系统精度。A new method based on Radial Basis Function（RBF） neural network was proposed to correct the system error of a optical encoder. The modeling method of RBF was introduced in detail and the theoretical basis for adjusting the parameters of the model was given. A new model for error correction was set up by taking the test values of the high precision instrument as outputs and the angle values of sample points as inputs. The testing results show that the precision of the encoder by this method has increased by 3 times as compared with that of traditional method and the precision of measuring system is improved greatly by using the RBF model as error compensation.

关 键 词：编码器 神经网络 误差修正 

分 类 号：TP212.12[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程]