A Temperature Error Parallel Processing Model for MEMS Gyroscope based on a Novel Fusion Algorithm
| العنوان: | A Temperature Error Parallel Processing Model for MEMS Gyroscope based on a Novel Fusion Algorithm |
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| المؤلفون: | Huiliang Cao, Chong Shen, Tiancheng Ma |
| المصدر: | Electronics, Vol 9, Iss 3, p 499 (2020) Electronics Volume 9 Issue 3 |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Boosting (machine learning), Computer Networks and Communications, Computer science, Noise reduction, adaptive boosting (adaboost), mems gyroscope, lcsh:TK7800-8360, 02 engineering and technology, law.invention, compensation, law, 0202 electrical engineering, electronic engineering, information engineering, denoising, AdaBoost, variational modal decomposition (vmd), Electrical and Electronic Engineering, Fitness function, 020208 electrical & electronic engineering, Vibrating structure gyroscope, lcsh:Electronics, Particle swarm optimization, Gyroscope, 021001 nanoscience & nanotechnology, Hardware and Architecture, Control and Systems Engineering, Signal Processing, 0210 nano-technology, Algorithm, immune based particle swarm optimization (ipso) |
| الوصف: | To deal with the influence of temperature drift for a Micro-Electro-Mechanical System (MEMS) gyroscope, this paper proposes a new temperature error parallel processing method based on a novel fusion algorithm. Firstly, immune based particle swarm optimization (IPSO) is employed for optimal parameters search for Variational Modal Decomposition (VMD). Then, we can get the optimal decomposition parameters, wherein permutation entropy (PE) is employed as the fitness function of the particles. Then, the improved VMD is performed on the output signal of the gyro to obtain intrinsic mode functions (IMFs). After judging by sample entropy (SE), the IMFs are divided into three categories: noise term, mixed term and feature term, which are processed differently. Filter the mixed term and compensate the feature term at the same time. Finally, reconstruct them and get the result. Compared with other optimization algorithms, IPSO has a stronger global search ability and faster convergence speed. After Back propagation neural network (BP) is enhanced by Adaptive boosting (Adaboost), it becomes a strong learner and a better model, which can approach the real value with higher precision. The experimental result shows that the novel parallel method proposed in this paper can effectively solve the problem of temperature errors. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2079-9292 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab4e8ec192120823eb3b3fdf0a1ccc9fTest https://www.mdpi.com/2079-9292/9/3/499Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....ab4e8ec192120823eb3b3fdf0a1ccc9f |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20799292 |
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