摘要
针对热电偶信号处理中的非线性校正和冷端补偿等突出问题,利用径向基函数(RBF)神经网络构造双输入单输出的网络模型,并采用遗传算法对网络结构和参数进行优化训练,同时完成了热电偶测温中的非线性校正和冷端补偿。经仿真实验证明:该方法的测量误差减小至0.095%,在较大范围内提高了热电偶温度测量的精度。
A method is presented to compensate non-linearity and cold-side-offset for signal processing of thermocouple. A network model with two inputs and single output is constructed by radial basis function （RBF） neural network （NN） ,which is trained by genetic algorithm. Under the NN model, non-linearity compensation and cold-side-offset adjustment of thermocouple are realized simultaneously. The simulation experimenls show that the testing error of this method is 0. 095 % ,it improves the accuracy in a wider range.
出处
《传感器与微系统》
CSCD
北大核心
2007年第1期36-38,共3页
Transducer and Microsystem Technologies
关键词
热电偶
径向基函数神经网络
遗传算法
非线性校正
冷端补偿
thermocouple
radial basis function （RBF） neural network （NN）
genetic algorithm
non-linearity adjustment
cold-side-offset
