ArticleSensors (Basel, Switzerland)2022
Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- AI Methods in Sensor Calibration.Sensors (Basel, Switzerland) · 2026Review
- The Parameter-Optimized Recursive Sliding Variational Mode Decomposition Algorithm and Its Application in Sensor Signal Processing.Sensors (Basel, Switzerland) · 2025Article
- Neural Network Methods in the Development of MEMS Sensors.Micromachines · 2024Review
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4 authors.
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Abstract
Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s
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