基于混合优化算法的销轴传感器温度补偿及应用

发布时间：2018-06-06 01:28

  本文选题：果蝇算法 + RBF神经网络　； 参考：《传感技术学报》2017年11期

【摘要】：针对应变片式销轴传感器井下工作过程中温度发生变化产生温度漂移,导致测量精度降低的问题,提出一种果蝇算法优化RBF神经网络的温度补偿模型,采用果蝇算法对神经网络的扩展参数进行全局优化,利用应力测试平台实测参数及神经网络非线性映射能力训练温度补偿模型。为验证温度补偿模型补偿效果及训练效率,对35℃下传感器进行实验测试。结果表明:35℃下,温度补偿模型补偿平均误差远小于单一算法补偿效果,验证了此方法具有较高的训练效率及补偿效果,能够提高传感器在不同温度、载荷作用下测量精度,同时将本文模型应用采煤机截割煤壁工作中,得到导向滑靴在采煤机行走截割煤壁过程中受力,为导向滑靴结构优化及提高采煤机可靠性和使用寿命提供依据。
[Abstract]:In order to solve the problem of temperature drift caused by temperature change in the working process of strain gauge pin shaft sensor, a temperature compensation model of Drosophila algorithm for optimizing RBF neural network is proposed. The expanded parameters of neural network are optimized globally by Drosophila algorithm, and the temperature compensation model is trained by using the measured parameters of stress test platform and the nonlinear mapping ability of neural network. In order to verify the compensation effect and training efficiency of the temperature compensation model, the sensor was tested at 35 鈩，

本文编号：1984373