基于鲁棒正则化极限学习机的电容层析成像图像重建

doi:10.3969/j.issn.1000-1158.2022.08.11

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Abstract Image reconstruction of electrical capacitance tomography is a nonlinear and ill-posed inverse problem. Robust regularized extreme learning machine combined with iterative reweighted least squares is proposed for the image reconstruction of electrical capacitance tomography. Oil/gas two-phase flow is selected as research object, and the randomly distribution flow regimes are obtained using finite element simulation. RELM-IRLS network is trained, and the reconstructed images are compared with Landweber iterative algorithm and extreme learning machine algorithm. The average error of the test set of the RELM-IRLS algorithm is reduced by 4.6% compared with extreme learning machine algorithm. Simulation and static experiment results show that the quality of the reconstructed images is improved obviously using RELM-IRLS algorithm, which also has good generalization ability.

Key words： metrology electrical capacitance tomography iterative reweight extreme learning machine Landweber iterative algorithm

Received: 29 January 2021 Published: 20 August 2022

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TB937

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	ZHANG Li-feng
	DAI Li

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ZHANG Li-feng,DAI Li. Image Reconstruction Based on Robust Regularized Extreme Learning Machine for Electrical Capacitance Tomography[J]. Acta Metrologica Sinica, 2022, 43(8): 1044-1049.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.08.11 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I8/1044

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