Pressure Sensor Error Compensation Algorithm Based on MEA-BP Neural Network

SHI Hao; FAN Hui; LI Jianchen; ZHAO Runhui; LI Ya

doi:10.11993/j.issn.2096-3920.202205002

Volume 31 Issue 2

Apr 2023

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Article Navigation > Journal of Unmanned Undersea Systems > 2023 > 31(2): 252-258

SHI Hao, FAN Hui, LI Jianchen, ZHAO Runhui, LI Ya. Pressure Sensor Error Compensation Algorithm Based on MEA-BP Neural Network[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 252-258. doi: 10.11993/j.issn.2096-3920.202205002

Citation:

SHI Hao, FAN Hui, LI Jianchen, ZHAO Runhui, LI Ya. Pressure Sensor Error Compensation Algorithm Based on MEA-BP Neural Network[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 252-258. doi: 10.11993/j.issn.2096-3920.202205002

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Pressure Sensor Error Compensation Algorithm Based on MEA-BP Neural Network

doi: 10.11993/j.issn.2096-3920.202205002

The 705 Research Institute, China State of Shipbuilding Corporation Limited, Xi’an 710077, China

Received Date: 2022-05-10
Accepted Date: 2022-06-28
Rev Recd Date: 2022-06-09

Available Online: 2023-02-21

Abstract

Abstract

Piezoresistive pressure sensors are sensitive to environmental changes. Ambient temperature changes would produce thermal drift, which affects sensor performance. This study entailed the use of the mind evolutionary algorithm(MEA)-back propagation(BP) neural network algorithm to establish an error compensation model for piezoresistive pressure sensors. The model uses the MEA algorithm to optimize the initial weight and threshold of the neural network, which reduces the possibility of the training falling into local optimization owing to the uncertainty of the initial value. The Levenberg–Marquardt algorithm replaces the gradient descent method to accelerate the convergence speed of the neural network and increase the reliability of the compensation algorithm. The results of the simulation show that, compared with those of the BP neural network compensation algorithm and genetic algorithm(GA)-BP neural network, the expectation of the root mean square error of the MEA-BP algorithm is lower by 48.7% and 8.29%, respectively. The standard deviations are reduced to 5% and 4% of those of the BP and GA-BP neural networks, respectively. This demonstrates that the BP neural network compensation method optimized by the MEA algorithm can compensate for the influence of temperature more accurately and that the compensation result is more reliable.
- pressure sensor,
- error compensation,
- mind evolutionary algorithm,
- back propagation neural network

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References(14)

References

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