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首页> 外文期刊>IEEE sensors journal >Adaptive Selection of Relaxation Factor in Landweber Iterative Algorithm
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Adaptive Selection of Relaxation Factor in Landweber Iterative Algorithm

机译：Landweber迭代算法中松弛因子的自适应选择

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摘要

It is crucial to select a suitable relaxation factor in Landweber iterative algorithm for electrical capacitance tomography, because it affects the convergence and convergence rate. Previous study shows that the relaxation factor should be selected adaptively according to the sensor structure (e.g., the number of electrodes), permittivity distribution, and noise level in capacitance data. With different number of electrodes and four typical permittivity distributions, the relaxation factor and the related convergence are investigated in consideration of the change in relative image error and relative capacitance residual. By adding noises with different levels to noise-free data, their influences on the selection of relaxation factor and convergence are characterized. For a typical permittivity distribution, the corresponding relaxation factor is selected based on the upper bound of all relaxation factors, which are determined by a sensor design. The performance of Landweber algorithm with an adaptively selected relaxation factor is compared with constant relaxation factors and updated relaxation factor, showing that the proposed method can ensure convergence with less computation time than other relaxation factors.
机译：在Landweber迭代算法中选择合适的弛豫因子进行电容层析成像至关重要，因为它会影响收敛性和收敛速度。先前的研究表明，应根据传感器结构（例如电极数量），介电常数分布和电容数据中的噪声水平来自适应选择松弛因子。对于不同数量的电极和四种典型的介电常数分布，考虑了相对图像误差和相对电容残余的变化，研究了弛豫因子和相关的收敛性。通过将不同级别的噪声添加到无噪声数据中，可以表征它们对松弛因子选择和收敛的影响。对于典型的介电常数分布，基于所有弛豫因子的上限选择相应的弛豫因子，该上限由传感器设计确定。将具有自适应选择的松弛因子的Landweber算法的性能与恒定的松弛因子和更新的松弛因子进行了比较，表明与其他松弛因子相比，该方法可以确保收敛，并且计算时间更少。

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