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The investigation of the regularization term in the continuous-parameter subspace based optimization method in reconstructing PEC objects

机译:基于连续参数子空间的优化方法在重建PEC对象中的正则项研究

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This paper investigates the influence of the regularization term on the performance of the continuous-parameter subspace based optimization method in reconstructing perfectly electric conductors (PEC), which provides a theoretical explanation to the choice of regularization term. The number of leading singular values of a current-to-field mapping operator is found to balance the accuracy and the stability of the solution. If the number of leading singular values is chosen as a large number, the noise is amplified in the inverse process. On the other hand, if this parameter is chosen to be a small number, the convergence of the optimization method will be slow. It is found that the regularization term in the PEC SOM behaves similarly as its counterpart in the dielectric case.
机译:本文研究了正则项对基于连续参数子空间的优化方法在重构完美电导体(PEC)中的性能的影响,为正则项的选择提供了理论解释。发现电流-场映射算子的前导奇异值的数量可以平衡解决方案的准确性和稳定性。如果将前导奇异值的数量选择为大数,则噪声会在逆过程中被放大。另一方面,如果将此参数选择为较小的值,则优化方法的收敛速度将很慢。发现PEC SOM中的正则项的行为与其在介电情况下的正则项相似。

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