Research on error correction model of surface acoustic wave yarn tension transducer based on DOA–SVR model

We present an error correction model for improving the accuracy of Surface Acoustic Wave Yarn Tension (SAWYT) transducer measurements. The model utilizes Support Vector Regression (SVR) and Dingo Optimization Algorithm (DOA). Significantly, the input comprises the oscillation frequencies from the co...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation Vol. 226; p. 114126
Main Authors: Liu, Shoubing, Wang, Dongqiang, Xing, Renzhou, Ren, Jiale, Lu, Wenke
Format: Journal Article
Language:English
Published: Elsevier Ltd 28.02.2024
Subjects:
Dingo optimization algorithm (DOA)
Error correction
Support vector regression (SVR)
Surface acoustic wave
Yarn tension transducer
Yarn tension transducer
Support vector regression (SVR)
Dingo optimization algorithm (DOA)
Error correction
Surface acoustic wave
ISSN:0263-2241
Online Access:Get full text
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Summary:We present an error correction model for improving the accuracy of Surface Acoustic Wave Yarn Tension (SAWYT) transducer measurements. The model utilizes Support Vector Regression (SVR) and Dingo Optimization Algorithm (DOA). Significantly, the input comprises the oscillation frequencies from the co-located measurement and calibration transducers. The model is trained using the yarn tension that bore on the measurement transducer as the output. As a Subsequently, for the training set, the DOA–SVR model achieves a mean square error (MSE) of 1.4999×10−5 and a determination coefficient (R2) of 0.99997, surpassing the other two models. On the test set, the DOA–SVR model continues to excel with an MSE of 3.8371×10−5 and an R2 of 0.99990, outperforming the other models. These results highlight the superior performance of the DOA–SVR model in error correction for SAWYT transducers, making it as the preferred choice for both the training and test sets. •Proposing the unique combination of sample data from co-located measurement and calibration transducers.•Introducing an SVR-based error correction model for a SAW yarn tension transducer.•Optimization of hyperparameters in SVR models using the DOA algorithm.
ISSN:0263-2241
DOI:10.1016/j.measurement.2024.114126