海南大学学报（自然科学版中英文）2026，Vol.44Issue(2)：204-216,13.DOI:10.65658/j.hndk.2025091802
基于混合核最小二乘支持向量机的S型热电偶高精度补偿方法
A nonlinear compensation method of thermocouple based on least squares support vector machine with hybrid kernels
摘要
Abstract
To address the pronounced nonlinear characteristics of thermocouple sensors,particularly for the poor linearity exhibited by Type S thermocouples over a wide temperature range of-50～1 768℃in aerospace engine temperature testing,this paper develops a nonlinear compensation model based on hybrid kernel least squares support vector machines to compensate the thermocouple measurement system.Firstly,a hybrid kernel function is constructed by combining radial basis function and polynomial kernels to overcome the inability of single kernel functions to balance local details and global mapping,thereby ensuring linear separability in high-dimensional feature spaces during data processing.Secondly,an improved particle swarm optimization algorithm is proposed for kernel parameter optimization.This enhanced particle swarm optimization incorporates a probability mutation strategy to strengthen its ability to escape local optima,along with adaptive inertia weight and learning factor update strategies designed to adjust the search space dynamically,ensuring both solution accuracy and convergence speed.Experimental results demonstrate that compared with the backpropagation neural network method,the proposed approach reduces the maximum absolute error by 0.260℃,achieving a post-compensation maximum fitting error of 0.380℃and an accuracy rate of 98.960%,meeting the stringent requirements for high-precision temperature measurements across broad ranges in practical aerospace engines.关键词
非线性补偿/混合核函数/概率突跳策略/高精度测试Key words
nonlinear compensation/hybrid kernel function/probability mutation strategy/high-precision measurement分类
社会科学引用本文复制引用
郭蓉,许东阳,杨林燚,唐浩,徐博..基于混合核最小二乘支持向量机的S型热电偶高精度补偿方法[J].海南大学学报（自然科学版中英文）,2026,44(2):204-216,13.基金项目
本文得到以下基金项目支持:国家自然科学基金项目(62563007)和国家自然科学基金项目(62563008). This work was supported by the following funding projects:National Natural Science Foundation of China(62563007) （62563007）
National Natural Science Foundation of China(62563008). （62563008）
