An extreme learning machine combined with Landweber iteration algorithm for the inverse problem of electrical capacitance tomography |
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| Affiliation: | 1. Department of Chemical Engineering, Yasouj University, Yasouj, Iran;2. Department of Mechanical Engineering, Yasouj University, Yasouj, Iran;3. Department of Electrical Engineering, Yasouj University, Yasouj, Iran;1. State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, 650093, PR China;2. Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, 650093, PR China;3. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, 78249, USA;4. National & Local Joint Engineering Research Center of Energy Saving, Environmental Protection Technology in Metallurgy and Chemical Engineering Industry, Kunming, 650093, PR China;5. Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming, 650093, PR China;1. School of Energy, Power and Mechanical Engineering, North China Electric Power University, Changping District, Beijing, 102206, China;2. Institute of Engineering Thermophysics, Chinese Academy of Sciences, Haidian District, Beijing, 100190, China;3. University of Chinese Academy of Sciences, Haidian District, Beijing, 100190, China;4. School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing, 102206, China |
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| Abstract: | 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. |
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| Keywords: | Electrical capacitance tomography (ECT) Ill-posed Extreme learning machine (ELM) Landweber iteration |
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