Kernel extreme learning machine combined with gray wolf optimization for temperature compensation in pressure sensors

• Autorzy: Wang Huan , Wu Ting , Liu Pan , Zou Yijun , Zeng Qinghua

Identyfikatory

DOI

10.24425/mms.2025.152773

• Języki publikacji: EN

Abstrakty

EN

In order to improve the measurement accuracy of pressure sensors, a method based on gray wolf optimization (GWO) to optimize kernel extreme learning machine (KELM) is proposed to address the problem of nonlinear drift that can be easily affected by temperature in the working environment. Firstly, the fast search capability of the GWO algorithm is used to find optimal regularization coefficients and kernel function parameters of the KELM algorithm; secondly, the random mapping of the traditional ELM algorithm is replaced by the kernel mapping of the KELM algorithm to improve the generalization and stability degradation brought by the random assignments. Finally, the voltage signal values under different temperature and pressure environments are obtained through calibration experiments and compensated by the GWO-KELM algorithm. The results show that the GWO-KELM method has a better compensation effect compared with the traditional BP neural network with a full-scale error of 0.13% (FS), the ELM algorithm with a full-scale error of 0.12%FS, and the KELM algorithm with a full-scale error of 0.12% in the range of 0 to 700 kPa absolute pressure and -40˚ to 70˚. The full-scale error is only 0.07% and the maximum absolute error is as low as 0.5446 kPa, which improves the accuracy index by one order of magnitude.

Słowa kluczowe

EN

gray wolf optimization; kernel extreme learning machine; pressure sensor; temperature compensation

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2025
• Tom: Vol. 32, nr 1
• Strony: 1--14
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr., wzory

Twórcy

autor

Wang Huan

• School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, China

autor

Wu Ting

• School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, China

• https://orcid.org/0000-0003-1691-984X

autor

Liu Pan

• School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, China

autor

Zou Yijun

• School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, China

autor

Zeng Qinghua

• School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, China

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Uwagi

This work was supported by the National Natural Science Foundation of China (No. 61174120) and the Scientific Research and Innovation Program of the Chinese Academy of Management Sciences (No. JKSC14568).