代数神经网络电阻层析成像图像重建算法

两相流体具有复杂性的流动特性，图像重建的精度是两相流参数准确测量的基础。针对电阻层析成像系统存在的软场特性、强非线性和不适定性，使得重建的图像质量差、计算时间长等问题，基于代数运算的神经网络，给出了一种基于代数神经网络电阻层析成像图像重建算法。该算法通过建立代数神经网络，以测量的边界电压值作为神经网络的输入，将图像重建转变为一个严格对角占优的线性方程组的求解问题，以达到图像快速、准确的重建目的。通过实验仿真分析，该方法具有收敛速度快、代价低和误差小等特点。

Algebraic neural network image reconstruction algorithm for electrical resistance tomography

Two-phase fluid has complex flow characteristic and the accurate identification of flow regime is the basis of the accurate measurement of two-phase flow's parameter.There are still many defects such as low reeonstraction quality and low reconstruction speed in image reconstruction algorithm because of soft field characteristic,strong nonlinear and ill-posedness of electrical resistance tomography.This paper puts forward a new image reconstruction algorithm for ERT based on algebraic neural network.This algorithm transforms image reconstruction into a problem of solving strictly diagonal-dominant linear equations.Through the simulation experiment analysis,this method has characteristics such as fast convergence,low cost and small error.