基于数据生成的压力传感器的准确非线性和温度补偿方法
Mingxuan Zou1, Ye Xu2, Jianxiang Jin1
1College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了一种新的方法,通过生成更多的校准数据来补偿压力传感器. 这种方法可以在没有复杂算法的情况下提高准确性,从而提高传感器的性能.
科学领域:
- 传感器技术 传感器技术
- 计量学 计量学 计量学
- 机器学习 机器学习
背景情况:
- 压电阻压力传感器有非线性和温度敏感性,需要复杂的补偿.
- 现有的软件补偿方法往往优先考虑复杂的算法,而不是校准数据质量和计算效率.
研究的目的:
- 开发一种用于利用数据生成的压力传感器的新型补偿方法.
- 通过使用更大的数据集而不是复杂的模型来提高测量准确性.
- 验证拟议方法的有效性和工程适用性.
主要方法:
- 一种使用aquila优化器优化混合多项式内核极端学习机器 (AO-MPKELM) 算法的新补偿方法.
- 通过学习传感器的校准过程来生成额外的校准数据.
- 进行详细的校准实验和废弃分析以评估性能.
主要成果:
- 生成的数据与真实数据具有很高的一致性,最大电压偏差为0.71 mV.
- 拟议的方法在与二线性插值相结合时,将测量误差降低了78.95%.
- 实现了0.03%全尺度 (FS) 的高精度.
结论:
- 提出的以数据为中心的补偿方法对于高精度的压力测量是有效的.
- AO-MPKELM算法和数据生成技术提供了卓越的工程应用性.
- 这种方法通过专注于数据增强来解决现有补偿方法的局限性.
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