Dynamic Inverse Model for Multi-sensor Measurement System Based on Wavelet Neural Network
As traditional measuring method based on dielectric coefficients shows cross-sensitivity for multi-factor in the measurement of oil/water mixture,it can not meet the requirements of digital oilfield construction.Therefore,this paper presents an inverse model of wavelet neural network (WNN) combining with multi-sensing technology for achieving high-accuracy measurement of water content in crude oil.The simulation and experimental results demonstrate that the proposed method is available to eliminate the cross-coupling effects of multi-factors.The method has higher measurement accuracy and stronger generalization than the model built by BP-NN,and opens a versatile approach in nonlinear error calibration for multi-factors measuring system.
Inverse model Multi-sensors Error calibration Dynamic measurement cross-coupling
Dongzhi Zhang Bokai Xia Kai Wang Jun Tong Nianzhen Yang
College of Information and Control Engineering, China University of Petroleum(East China),Qingdao, Shandong, 266555, China
国际会议
杭州
英文
335-339
2012-03-23（万方平台首次上网日期，不代表论文的发表时间）