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Temperature Compensation Method Based on an Improved Firefly Algorithm Optimized Backpropagation Neural Network for

Libin Huang1,2, Lin Jiang1,2, Liye Zhao1,2

  • 1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

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|July 27, 2022
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Summary

This study introduces an improved firefly algorithm-backpropagation (IFA-BP) neural network for temperature compensation in micromachined silicon resonant accelerometers. The IFA-BP model significantly enhances accelerometer performance by reducing temperature-induced drift.

Keywords:
firefly algorithmmicromachined silicon resonant accelerometerneural networktemperature compensation

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Area of Science:

  • * Instrumentation and Measurement
  • * Artificial Intelligence in Engineering
  • * Materials Science and Engineering

Background:

  • * Micromachined Silicon Resonant Accelerometers (MSRA) exhibit significant output drift in variable temperature environments.
  • * Temperature-induced errors degrade accelerometer performance, necessitating effective compensation strategies.

Purpose of the Study:

  • * To develop and validate an improved firefly algorithm-backpropagation (IFA-BP) neural network for temperature compensation of MSRA.
  • * To enhance the accuracy and robustness of temperature compensation compared to existing methods.

Main Methods:

  • * Development of an Improved Firefly Algorithm (IFA) to optimize the initial weights and thresholds of a Backpropagation (BP) neural network.
  • * Training and evaluation of the IFA-BP model using zero-bias and full-temperature experimental data from an MSRA.
  • * Comparative analysis against the standard Firefly Algorithm-Backpropagation (FA-BP) model.

Main Results:

  • * The IFA-BP model demonstrated superior temperature compensation performance over the FA-BP model.
  • * Zero-bias stability at room temperature improved by over an order of magnitude post-compensation.
  • * In the -40°C to 60°C range, scale factor variation improved >70 times, and bias variation improved ~3 orders of magnitude.

Conclusions:

  • * The proposed IFA-BP neural network effectively compensates for temperature-induced drift in MSRAs.
  • * This method significantly enhances accelerometer stability and accuracy across a wide temperature range.
  • * The IFA-BP approach offers a robust solution for improving accelerometer performance in demanding environments.