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Nonlinear Decoupling Study of Six-Axis Acceleration Sensor Based on Improved BP Neural Network.

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This study enhances back propagation (BP) neural networks for parallel six-axis accelerometers, significantly improving measurement accuracy by reducing nonlinear coupling errors. The new model offers superior precision for high-performance inertial navigation systems.

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gradient descent with momentumimproved BP neural networknonlinear decouplingparallel mechanismsix-axis accelerometer

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Area of Science:

  • * Measurement Science
  • * Sensor Technology
  • * Artificial Intelligence

Background:

  • * Nonlinear coupling errors affect the accuracy of parallel six-axis accelerometers.
  • * Existing linear decoupling methods are insufficient for complex error sources.

Purpose of the Study:

  • * To develop an improved back propagation (BP) neural network decoupling model.
  • * To enhance the convergence speed, shock resistance, and accuracy of accelerometer measurements.
  • * To provide a robust solution for nonlinear coupling errors in six-axis accelerometers.

Main Methods:

  • * Improvement of the back propagation (BP) neural network by incorporating gradient descent with momentum and the Levenberg-Marquardt (LM) algorithm.
  • * Training the improved BP neural network model using calibration data from a mid-frequency standard vibration device (APS 129 ELECTRO-SEIS).
  • * Nonlinear decoupling of a test set using the trained improved BP neural network model.

Main Results:

  • * The improved BP neural network model achieved high decoupling accuracy for six-axis accelerometers.
  • * Acceleration measurement accuracies of 0.035% (x-axis), 0.018% (y-axis), and 0.039% (z-axis) were obtained.
  • * Significant improvement in sensor measurement accuracy compared to traditional linear decoupling methods.

Conclusions:

  • * The proposed improved BP neural network model effectively addresses nonlinear coupling errors in six-axis accelerometers.
  • * The model demonstrates superior decoupling accuracy and enhances overall sensor performance.
  • * This research offers valuable theoretical support for the development of high-precision inertial navigation systems.