Liu Shoubing, Xue Peng, Lu Jinyan, Lu Wenke
School of Electrical and Information Engineering, Henan University of Engineering, Zhengzhou, 451191, China.
School of Information Science and Technology, Donghua University, Shanghai, 201620, China.
Ultrasonics. 2021 Sep;116:106511. doi: 10.1016/j.ultras.2021.106511. Epub 2021 Jul 2.
With the rapid growth of the SAW (Surface Acoustic Wave) yarn tension sensor, the requirement for its measurement accuracy is higher and higher. However, little research has been conducted in this field. Thus, this paper studies this field and provides a solution. This paper firstly investigates the principle and training of PSO-SVR model. On this basis, this paper also studies the association of output frequency difference data with the matching yarn tension exerted on the SAW yarn tension sensor. After that, employing the frequency difference data as input and corresponding tension as output, the PSO-SVR model is trained and employed to predict output tension of the sensor. Finally, the error with actually applied tension was calculated, the same in the least-squares approach and the BP neural network. By multiple comparisons of the same sample data set in the overall, as well as the local accuracy of the forecasted results, it is easy to confirm that the output error forecast by PSO-SVR model is much smaller relative to the least-squares approach and BP neural network. As a result, a new way for the data analysis of the SAW yarn tension sensor is provided.
随着声表面波（SAW）纱线张力传感器的迅速发展，对其测量精度的要求越来越高。然而，该领域的研究却很少。因此，本文对该领域展开研究并提供了一种解决方案。本文首先研究了粒子群优化支持向量回归（PSO-SVR）模型的原理及训练。在此基础上，本文还研究了输出频率差数据与施加在SAW纱线张力传感器上的匹配纱线张力之间的关联。之后，以频率差数据作为输入，相应的张力作为输出，对PSO-SVR模型进行训练并用于预测传感器的输出张力。最后，计算了与实际施加张力的误差，最小二乘法和BP神经网络的情况相同。通过对同一样本数据集的整体多次比较以及预测结果的局部准确性比较，很容易确认PSO-SVR模型预测的输出误差相对于最小二乘法和BP神经网络要小得多。结果，为SAW纱线张力传感器的数据分析提供了一种新方法。
