ArticleSensors (Basel, Switzerland)2022
Machine Learning and Swarm Optimization Algorithm in Temperature Compensation of Pressure Sensors.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- An Intelligent Temperature Compensation Method for Pressure Sensors Under High-Temperature and High-Pressure Conditions Based on a Modified Slime Mold Algorithm.Micromachines · 2026Article
- Accurate Measurement of Blast Shock Wave Pressure by Enhanced Sensor System Based on Neural Network.Sensors (Basel, Switzerland) · 2025Article
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Abstract
The main temperature compensation method for MEMS piezoresistive pressure sensors is software compensation, which processes the sensor data using various algorithms to improve the output accuracy. However, there are few algorithms designed for sensors with specific ranges, most of which ignore the operating characteristics of the sensors themselves. In this paper, we propose three temperature compensation methods based on swarm optimization algorithms fused with machine learning for three different ranges of sensors and explore the partitioning ratio of the calibration dataset on Sensor A. The results show that different algorithms are suitable for pressure sensors of different ranges. An optimal compensation effect was achieved on Sensor A when the splitting ratio was 33.3%, where the zero-drift coefficient was 2.88 × 10
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