文章基本信息

标题：Impact of the Radial Basis Function Spread Factor onto Image Reconstruction in Electrical Impedance Tomography ∗
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作者：Marketa Michalikova ; Michal Prauzek ; Jiri Koziorek 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：4
页码：230-233
DOI：10.1016/j.ifacol.2015.07.038
语种：English
出版社：Elsevier
摘要：AbstractThe major problem of the Electrical impedance tomography (EIT) is to get the resistivity distribution image of a given cross-sectional area. There are many methods solving this non-linear problem, mostly requiring certain simplifications and assumptions. Most of the methods are also computationally demanding and not easy to implement. The usage of the neural networks appears to be a solution of the mentioned problems. In this article we continued with our previous study and used Radial basis function (RBF) neural network for image reconstruction in electrical impedance tomography and we focused on examining how the change of the spread parameter of the RBF influences the result of the image reconstruction with the RBF neural network.
关键词：KeywordsElectrical impedance tomographyRadial basis function neural network