基于GA-WNN的电涡流传感器的温度补偿

针对电涡流传感器的温度漂移对其测量精度带来较大影响的问题,提出了基于遗传优化小波神经网络(GA-WNN)算法对电涡流传感器进行温度补偿修正模型。通过对电涡流传感器做标定实验,并且利用LM35温度传感器监测其工作温度,建立GA-WNN神经网络模型。该模型用遗传算法对小波神经网络的权、阈值进行全局的优化,改善了小波神经网络训练速度慢的问题,克服了易陷入局部最优的缺陷。研究结果表明,补偿后的灵敏度温度系数由8.69×10^-3/℃提升到3.48×10^-4/℃;零位温度系数由4. 78×10^-3/℃提升到1.85×10^-4/℃,均提高了一个数量级,成功实现了温度补偿的目的。

Temperature Compensation of Eddy Current Sensor Based on GA-WNN Algorithm

In order to solve the problem of temperature drift of eddy current sensors, a temperature compensation correction model based on genetic optimization wavelet neural network(GA-WNN) algorithm for eddy current sensor is persented. The GA-WNN neural network model was established by calibrating the eddy current sensor and monitoring its working temperature with LM35 temperature sensor. The model uses genetic algorithm to optimize the weight and threshold of the wavelet neural network, which improves the slow training speed of the wavelet neural network and overcomes the defect that is easy to fall into local optimum. The results show that the sensitivity temperature coefficient after compensation is raised from 8. 69×10^-3/℃ to 3. 48 × 10^-4/℃and the zero temperature coefficients is raised from 4. 78 ×10^-3/℃ to 1. 85 × 10^-4/℃. Both have increased by one order of magnitude, and successfully achieved temperature compensation.