基于LS-SVM的温度传感器非线性关系拟合及参考端温度补偿

文章编号: 提出一种基于最小二乘支持向量机(LS-SVM)的温度传感器非线性关系拟合模型，并根据温度传感器的输入输出特性给出两种方案对参考端温度进行补偿. 建立了LS-SVM回归模型，利用LS-SVM超强的学习能力对温度与电势间的非线性关系进行精确拟合. 两种方案均可对参考端温度进行有效补偿，其中方案2 可根据参考端温度、传感器实测电势对实际温度直接拟合，简化了补偿过程，提高了识别精度. 实验表明，LS-SVM回归法及文中所提出的补偿方案能很好地逼近实际温度，提高测量精度.

Fitting of Non-linear Relation of Temperature Sensor and Reference Temperature Compensation Based on LS-SVM

This paper proposes a model to fit the non-linear relation of temperature sensor based on least square support vector machine (LS-SVM), and presents two design schemes to compensate temperature of reference point according to the sensor's input/output characteristics. An LS-SVM regression model is set up, which takes advantage of the super learning capacity of LS-SVM to precisely fit the non-linear relation between temperature and voltage. Both schemes can effectively compensate reference point temperature. The second scheme can directly fit the actual temperature in accordance with the reference temperature and the actual measuring voltage of the sensor, simplifying the compensation and enhancing recognition precision. Experiments show that the LS-SVM regressing method and the proposed compensation method can fit the actual temperature and enhance measuring precision.