Identification on Dynamic Inverse Model for Sensor Based on Genetic Neural Network
The oil/water two-phase flow is a complicated two-component nonlinear system with time-variance, and the dynamic measuring system for water content in crude oil based on method of dielectric coefficient is affected by manufacturing technology of sensor itself and some non-object parameters, such as temperature and salinity content in oil-water mixture. Consequently, the sensor has serious non-linearity in its input-output characteristics, which is hard to be described by traditional mathematic models up to now. In this paper, a dynamic inverse model and its identification based on genetic neural network (GNN) is proposed for dealing with sensing mechanism under multi-factor influence, making full use of GNN’s advantages of nonlinear approximations with high accuracy, fast global convergence, self-adaptive and self-learning. The simulation result shows this method is effective to realize dynamic nonlinear error correction and eliminate the interference of non-object parameters and nonlinearity of sensor itself on the measurement, improving the nonlinear characteristics of the sensor and measuring accuracy for the dynamic testing system.
Zhang Dongzhi Hu Guoqing
School of Mechanical and Automotive Engineering South China University of Technology Guangzhou,510641,P.R.China
国际会议
深圳
英文
853-856
2008-12-10（万方平台首次上网日期，不代表论文的发表时间）