柔性力敏传感器的快速标定方法的研究
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TN301.1

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Fast calibration method for flexible force sensors
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    摘要:

    由于柔性力敏传感器阵列敏感点多,每个敏感点输出值差异大,为了解决传感器标定复杂的问题,给出了一种基于BP神经网络建立一个标定分类模型的快速标定方法,运用面积法给传感器阵列上的敏感点标定曲线分类,使用BP神经网络训练出分类模型,再运用最小二乘法拟合出每一类的标定曲线。通过验证样本检查了分类模型的分类正确率达到98%以上,实验验证了算法的标定结果准确性,标定时间小于1 s。实验结果表明,该方法对大面积柔性力敏传感器的标定效果理想,标定速度快,可以用来做柔性力敏传感器的标定。

    Abstract:

    Due to the large number of sensitive points of the flexible force sensitive sensor array, the output value of each sensitive point varies greatly. In order to solve the complex problem of sensor calibration, a fast calibration method based on BP neural network to establish a calibration classification model is given. The classification of sensitive points on the sensor array was calibrated. The BP neural network was used to train the classification model, and then the least squares method was used to fit each type of calibration curve. Through the validation sample, the classification accuracy of the classification model is over 98%. The experiment verifies that the calibration result of the algorithm is accurate. Calibration time is less than 1 s. The experimental results show that this method is ideal for calibration of largearea flexible force sensor, and it can be used to calibrate the flexible force sensor.

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吴云杰,徐超,周旭,杨先军,姚志明,孙怡宁.柔性力敏传感器的快速标定方法的研究[J].电子测量技术,2019,42(1):78-82

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  • 在线发布日期: 2021-05-19
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