Parameter Identification by Iterative Constrained Regularization

5th International Workshop on New Computational Methods for Inverse Problems

物理学;计算机科学

Zama, Fabiana^1

Department of Mathematics, P.zza Porta S. Donato 5, Bologna, Italy^1

关键词: Constrained problem;    ILL-posed inverse problem;    Numerical experiments;    Prior information;    Regularization methods;    Regularization terms;    Stable solutions;    Weighted constraints;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/657/1/012002/pdf
DOI  :  10.1088/1742-6596/657/1/012002

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Parameter identification from noisy data is an ill-posed inverse problem and data noise leads to poor solutions. Regularization methods are necessary to obtain stable solutions. In this paper we introduce the regularization by means of an iteratively weighted constraint and define an algorithm to compute the weights and solve the constrained problems using as prior information the given measurements. Although this approach is general, in the present work we prove the convergence in the case of least squares data fit with 2regularization term. The data reported in the numerical experiments prove the efficiency and good quality of the results.