核心回归的应用,用于改进传感介面测量系统的感应
Ana Dinora Guzman-Chavez1, Everardo Vargas-Rodriguez1
1Departamento de Estudios Multidisciplinarios, Universidad de Guanajuato, Yuriria 38940, Mexico.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
核心回归 (KRR) 显著提高了多层干扰度传感器测量范围. 与传统方法相比,这种机器学习方法将温度传感能力扩大了8倍.
科学领域:
- 光电学是指光电子产品.
- 机器学习 机器学习
- 传感器技术 传感器技术
背景情况:
- 干涉度传感器具有高灵敏度,但由于使用传统线性方法的测量范围较短,它们受到限制.
- 在干涉测量系统中确定测量和参数的现有技术通常依赖于线性灵敏度,限制了它们的有效运行周期.
- 多层干扰度传感器是有价值的,但在实现传统分析的广泛测量范围方面面临挑战.
研究的目的:
- 研究机器学习技术Kernel Ridge Regression (KRR) 的应用,以提高多层干扰度传感器的测量范围.
- 为了展示干涉测量系统的光谱特征如何利用KRR进行增强的参数估计.
- 评估KRR内部不同内核功能的有效性,用于温度传感应用.
主要方法:
- 利用光谱特征,特别是波长位置和干扰光谱的峰幅,作为机器学习模型的输入.
- 应用内核回归 (KRR) 具有四个不同的内核功能,以根据光谱特征估计温度.
- 专注于使用内核函数转换光谱特征,以实现非线性回归以提高传感器性能.
主要成果:
- 核心回归 (KRR) 通过使用多层干扰度传感器的光谱特征成功估计了温度.
- 使用高斯核实现了KRR,在温度测量中实现了0.094°C的平方根平均误差.
- 与传统方法相比,温度的测量范围被扩大了8倍,从有限的范围到4.550°C.
结论:
- 核心回归 (KRR) 是一种有效的机器学习方法,用于扩大干扰度传感器的测量范围.
- 在KRR中使用光谱特征和内核函数为传感系统中准确的温度估计提供了强大的方法.
- 这项研究表明,通过先进的机器学习,通过克服传统的范围限制,干扰度传感器技术取得了重大进展.
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