Abstract
Abstract
The detection accuracy of a yarn tension sensor using surface acoustic wave devices has become increasingly important. We investigate a nonlinear compensation scheme based on sparrow search algorithm (SSA) and support vector regression (SVR) models to improve its detection accuracy, and the principle of SSA–SVR model and training method are also explored. We take the output frequency of the two sensors as input, the yarn tension applied to the working sensor as output, train an SSA–SVR model and use it for nonlinear compensation. We analyze and calculate the linearity, compensation accuracy and robustness of the SSA-SVR model, and compared it with the multiple regression model and BP neural network. The comparison results show that the SSA–SVR model has the best linearity, the highest compensation accuracy and the most robust. Finally, a novel nonlinear compensation scheme is proposed.
Funder
the applied basic research project of ‘‘the textile light ’’
the doctoral foundation of the Henan University of Engineering
National Natural Science Foundation of China
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)
Cited by
2 articles.
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