| 论文标题 |
An extreme learning machine combined with Landweber iteration algorithm for the inverse problem of electrical capacitance tomography |
| 作者 |
Liu, X ;Wang, XX ; Hu, HL ; Li，L;Yang，XY |
| 发表/完成日期 |
2015-07-26 |
| 期刊名称 |
Flow Measurement and Instrumentation |
| 期卷 |
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| 相关文章 |
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| 论文简介 |
The image reconstruction of the electrical capacitance tomography (ECT) is an ill-posed and sparse
problem. In order to increase the accuracy and speed of the image reconstruction, this paper proposes a
new reconstruction algorithm which is based on the extreme learning machine (ELM) with the Landweber iteration method. Firstly, a nonlinear mapping model is established between the pixel gray-scale
values and the interelectrode capacitances by using the ELM which has a good learning ability and high
speed. Secondly, the Landweber iteration method, which has a good performance in convergence and
stability, is applied to calculate the output weight matrix of ELM. Finally, a convergence and stable
mapping model of ELM with the Landweber iteration algorithm (L-ELM) for ECT image reconstruction is
trained on Matlab platform. Both simulation and measurement tests are carried out to evaluate and
analyze the proposed method. Experimental results indicate that the proposed algorithm has good
generalization ability and high image reconstruction quality which are better than those of conventional
ELM algorithm. |
(创新港)
