基于机器学习的温度传感器的校准
Ce Liu1,2, Chunyuan Zhao2, Yubo Wang3
1College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
人工神经网络 (ANN) 模型提高了工业应用的温度传感器准确性. 与线性和多项式回归相比,ANN提供了更好的校准,但需要仔细的样本设计以防止过度拟合.
科学领域:
- 仪器仪表和测量仪器的使用
- 计算科学 计算科学
- 工业工程 工业工程 工业工程
背景情况:
- 精确的温度测量对于工业生产的质量和安全至关重要.
- 现有的温度传感器需要精确的校准才能达到最佳性能.
- 传统的校准方法可能缺乏要求高的应用所需的准确性.
研究的目的:
- 调查人工神经网络 (ANN) 模型对校准温度传感器的有效性.
- 将ANN校准性能与传统的线性和多项式回归技术进行比较.
- 确定成功基于ANN的温度传感器校准的关键因素.
主要方法:
- 从标准传感器收集各种环境条件的温度数据.
- 开发并应用了一个人工神经网络 (ANN) 模型用于传感器校准.
- 对比ANN,线性回归和多项式回归模型的校准精度.
主要成果:
- ANN模型校准显著提高了温度传感器的准确性.
- 在线回归和多项式回归相比,ANN模型显示出更高的校准性能.
- 确定了与小样本大小或杂数据相关的过度装配风险.
结论:
- 人工神经网络为温度传感器提供了更准确的校准方法.
- 训练样本的精心设计和模型参数的调整对于有效的ANN校准至关重要.
- 这项研究为工业温度测量应用提供了宝贵的技术支持.
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