Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network
Aiming at the problem of nonlinear coupling error in the measurement of parallel six-axis accelerometers, this study improves the back propagation (BP) neural network and proposes an improved BP neural network decoupling model that introduces the gradient descent with momentum and the Levenberg–Marq...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2280 |
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| author | Jialin Zhang Chunzhan Yu Chengxin Du Zhe Hao Zhibo Sun |
| author_facet | Jialin Zhang Chunzhan Yu Chengxin Du Zhe Hao Zhibo Sun |
| author_sort | Jialin Zhang |
| collection | DOAJ |
| description | Aiming at the problem of nonlinear coupling error in the measurement of parallel six-axis accelerometers, this study improves the back propagation (BP) neural network and proposes an improved BP neural network decoupling model that introduces the gradient descent with momentum and the Levenberg–Marquardt (LM) algorithm. By introducing the momentum factor in the model updating stage, the LM algorithm is used in the local learning stage to improve the convergence speed and shock resistance of the network, and to enhance the accuracy of the algorithm. Based on the mid-frequency standard vibration device APS 129 ELECTRO-SEIS (SPEKTRA, Stuttgart, Baden-Württemberg, Germany), the calibration data are obtained and the improved BP neural network decoupling model is trained to complete the nonlinear decoupling of the test set. Compared with the linear decoupling method, the decoupled six-axis accelerometers with the improved BP neural network model have acceleration measurement accuracies of 0.035%, 0.018% and 0.039% along the x, y and z axes, respectively, which indicates that the model has high decoupling accuracy, and it can significantly improve the measurement accuracy of the sensors. The research results can provide theoretical support for high-precision inertial navigation. |
| format | Article |
| id | doaj-art-cf7ba6dd0f1e4736add812b18fbc69e2 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-cf7ba6dd0f1e4736add812b18fbc69e22025-08-20T03:08:54ZengMDPI AGSensors1424-82202025-04-01257228010.3390/s25072280Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural NetworkJialin Zhang0Chunzhan Yu1Chengxin Du2Zhe Hao3Zhibo Sun4The School of Technology, Beijing Forestry University, Beijing 100083, ChinaThe School of Technology, Beijing Forestry University, Beijing 100083, ChinaThe School of Technology, Beijing Forestry University, Beijing 100083, ChinaThe School of Technology, Beijing Forestry University, Beijing 100083, ChinaBeihang School, Beihang University, Beijing 100191, ChinaAiming at the problem of nonlinear coupling error in the measurement of parallel six-axis accelerometers, this study improves the back propagation (BP) neural network and proposes an improved BP neural network decoupling model that introduces the gradient descent with momentum and the Levenberg–Marquardt (LM) algorithm. By introducing the momentum factor in the model updating stage, the LM algorithm is used in the local learning stage to improve the convergence speed and shock resistance of the network, and to enhance the accuracy of the algorithm. Based on the mid-frequency standard vibration device APS 129 ELECTRO-SEIS (SPEKTRA, Stuttgart, Baden-Württemberg, Germany), the calibration data are obtained and the improved BP neural network decoupling model is trained to complete the nonlinear decoupling of the test set. Compared with the linear decoupling method, the decoupled six-axis accelerometers with the improved BP neural network model have acceleration measurement accuracies of 0.035%, 0.018% and 0.039% along the x, y and z axes, respectively, which indicates that the model has high decoupling accuracy, and it can significantly improve the measurement accuracy of the sensors. The research results can provide theoretical support for high-precision inertial navigation.https://www.mdpi.com/1424-8220/25/7/2280six-axis accelerometerparallel mechanismimproved BP neural networkgradient descent with momentumnonlinear decoupling |
| spellingShingle | Jialin Zhang Chunzhan Yu Chengxin Du Zhe Hao Zhibo Sun Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network Sensors six-axis accelerometer parallel mechanism improved BP neural network gradient descent with momentum nonlinear decoupling |
| title | Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network |
| title_full | Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network |
| title_fullStr | Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network |
| title_full_unstemmed | Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network |
| title_short | Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network |
| title_sort | nonlinear decoupling study of six axis acceleration sensor based on improved bp neural network |
| topic | six-axis accelerometer parallel mechanism improved BP neural network gradient descent with momentum nonlinear decoupling |
| url | https://www.mdpi.com/1424-8220/25/7/2280 |
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