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Locating Impedance Change in Electrical Impedance Tomography Based on Multilevel BP Neural Network

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【作者】 彭源莫玉龙

【Author】 PENG Yuan MO Yu-LongSchool of Communication and Information Engineering of Shanghai University, Shanghai 200072, China

【机构】 School of Communication and Information Engineering of Shanghai UniversityShanghai 200072ChinaChina

【摘要】 <正> Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object’s periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the

【Abstract】 Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object’s periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.

【基金】 National Natural Science Foundation of China (Grant No. 60075009)
  • 【文献出处】 Journal of Shanghai University ,上海大学学报(英文版) , 编辑部邮箱 ,2003年03期
  • 【分类号】TP183
  • 【下载频次】13
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