利用神经网络提高编码器精度的方法 -

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利用神经网络提高编码器精度的方法

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摘要

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。
- 编码器/
- 神经网络/
- 误差修正
Abstract: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.
- encoder/
- neural network/
- error correction