Fitting analysis and research of measured data of SAW yarn tension sensor based on PSO–SVR model

Abstract

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.