摘要
针对压力传感器受温度漂移影响而造成的精度和可靠性下降的问题,建立粒子群优化算法与支持向量回归机结合的PSO-SVR温度补偿模型。利用PSO优化SVR的惩罚系数和核参数,改善粒子陷入局部最小值的问题。通过对测试集预测,得到最大绝对误差为0.001 6,均方误差为0.000 8%。PSO-SVR模型的补偿精度比RBF网络和SVM高。PSO-SVR模型能够满足实际使用的精度要求。
Aiming at the problem that the decrease of accuracy and reliability caused by temperature drift of pressure sensor,a PSO?SVR temperature compensation model combining particle swarm optimization algorithm with support vector regression ma?chine algorithm was proposed.The penalty coefficient and kernel function parameter of SVR were optimized by using PSO to im?prove the problem that the particles fell into local minima.Through the prediction of the test set,the maximum absolute error of the experiment is 0.001 6 and the mean square error is 0.000 8%.The compensation accuracy of PSO?SVR model is higher than that of RBF network and SVM.PSO?SVR model can satisfy the actual accuracy requirements.
作者
韩欣玉
何平
潘国峰
刘一赛
张万发
HAN Xin-yu;HE Ping;PAN Guo-feng;LIU Yi-sai;ZHANG Wan-fa(School of Computer Science and Engineering,Hebei University of Technology,Tianjin 300401,China;School of Electronic and Information Engineering,Hebei University of Technology,Tianjin 300401,China)
出处
《仪表技术与传感器》
CSCD
北大核心
2018年第8期9-12,共4页
Instrument Technique and Sensor
基金
国家科技重大专项(2016ZX02301003-004-007)
河北省高等学校科学技术研究重点项目(ZD2016123)
