霍尔电压传感器纹波效应及非线性误差的综合校正

某些霍尔电压传感器在测试含有高频谐波的直流电压量时,存在着由纹波效应等原因引起的严重非线性误差问题,本文提出一种基于神经网络信息融合技术的传感器误差综合校正法.该法直接从传感器输出信号中提取纹波电压特征量,不需要附加检测传感器.将纹波电压作为非目标参量,输入电压作为目标参量,理想输出作为目标值,通过神经网络的训练后,获得了校正后网络的权值和阀值.仿真实验结果表明,采取从输出信号中提取融合信息的方式,利用神经网络的信息融合功能,能逼近一个校正平面,从而较好地解决了传感器误差综合校正问题.

The ripple effect and nonlinear error comprehensive correction of the Hall voltage sensor

Some of Hall voltage mutual inductors have serious error caused by ripple effect etc when DC voltage with HF harmonic is measured. In the face of this question, a sort of error comprehensive correcting method which is based on information fusion technology of the neural network is presented. On this way , the ripple voltage character parameter is picked-up directly from output signal of voltage sensor and the added sensor is not need. Regarding the ripple voltage as no-target parameter and input voltage as target parameter and ideal output as target value, an artificial neural network (ANN) is structured and trained. The weights and offsets of each layer are obtained after training the ANN. The simulating experimental results demonstrate that ANN can approach to a correcting plane and solve preferably the error -correction question of the Hall voltage sensor.