Dynamic Inverse Model for Multi-Sensor Measurement System Based on Wavelet Neural Network

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.

• Periodical: Advanced Materials Research (Volume 499)
• Pages: 335-339
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.499.335
• Online since: April 2012
• Authors: Dong Zhi Zhang, Bo Kai Xia, Kai Wang, Jun Tong, Nian Zhen Yang
• Keywords: Cross-Coupling, Dynamic Measurement, Error Calibration, Inverse Model, Multi-Sensors

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