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The Implementation of FEM and RBF Neural Network in EIT

机译:EIT中FEM和RBF神经网络的实现

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With the rapid development of electronic technology, semiconductor section resistivity measurement is receiving increasing attention. This paper applies electrical impedance tomography (EIT) technology to semiconductor resistivity measurements. FEM is applied to solve the EIT forward problem. Mathematical description of partial differential equation, equivalent variation differential problem, element characteristic matrix and the assembly rule of general matrix are given for calculation. To solve the EIT inverse problem, a new method of Image reconstruction algorithm based on RBF neural network is proposed. This method can well adapt to non-linear and ill-posed characteristics of EIT. The simulation experiment results indicate that the RBF algorithm can improve the reconstruction imageȁ9;s quality and the accuracy obviously.
机译:随着电子技术的飞速发展,半导体截面电阻率的测量越来越受到人们的关注。本文将电阻抗层析成像(EIT)技术应用于半导体电阻率测量。有限元法用于解决企业所得税前转问题。给出了偏微分方程,等价变分微分问题,单元特征矩阵和通用矩阵的装配规则的数学描述。为解决EIT逆问题,提出了一种基于RBF神经网络的图像重建算法。该方法可以很好地适应EIT的非线性和不适定特性。仿真实验结果表明,RBF算法可以显着提高重建图像的质量和准确性,达到9ȁ。

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