摘要
为了消除热电偶测温过程中因冷端温度变化和非线性热电特性而引起的测温误差,提出了一种鲸鱼优化算法核极限学习机(WOA-KELM)的热电偶非线性补偿方法。利用鲸鱼算法对核极限学习机的参数进行搜索寻优,构建热电偶输入输出模型,同时完成热电偶的非线性校正和冷端补偿。利用S型热电偶的热电势-温度分度表数据进行模型训练,将训练结果与传统的最小二乘支持向量机(LSSVM)方法和径向基函数(RBF)神经网络进行比较,结果表明,所提方法具有更快的训练速度和更高的拟合精度。
In order to eliminate temperature measurement errors caused by temperature variation at the cold side and nonlinear thermoelectric characteristics in the thermocouple temperature measurement process,a nonlinear compensation method for thermocouple sensor based on whale optimizing algorithm kernel extreme learning machine(WOA-KELM)is proposed.This method uses whale algorithm to search and optimize on parameters of the kernel extreme learning machine to construct input-output model for thermocouple,at the same time,the non-linear correction and cold-junction-compensation of the thermocouple are completed.The training results of the proposed model based on the thermoelectric potential-temperature scale data of S-type thermocouple are compared with the traditional LSSVM method and RBF neural network,which verify the faster training speed and higher fitting precision of the proposed method.
作者
黄辉先
张广炎
陈思溢
胡拚
HUANG Huixian;ZHANG Guangyan;CHEN Siyi;HU Pin(School of Information Engineering,Xiangtan University,Xiangtan 411105,China)
出处
《传感器与微系统》
CSCD
2020年第5期24-26,29,共4页
Transducer and Microsystem Technologies
基金
国家部委预先研究基金资助项目(20170101)。
关键词
鲸鱼优化算法
核极限学习机
热电偶
非线性补偿
whale optimizing algorithm(WOA)
kernel extreme learning machine
thermocouple
nonlinear compensation
