Dynamic Inverse Model for Multi-Sensor Measurement System Based on Wavelet Neural Network
Year: 2012
Type: article
Abstract: 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.
Source: Advanced materials research
Authors: Dong Zhi Zhang, Bo Kai Xia, Kai Wang, Jun Tong, Nian Zhen Yang
Institution: China University of Petroleum, East China
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2086609730