Applications of Haar basis method for solving some ill-posed inverse problems

被引:16
|
作者
Pourgholi, R. [1 ]
Tavallaie, N. [1 ]
Foadian, S. [1 ]
机构
[1] Damghan Univ, Sch Math & Comp Sci, Damghan, Iran
关键词
Ill-posed inverse problems; Haar basis method; Tikhonov regularization method; Noisy data; GENERALIZED CROSS-VALIDATION; HEAT-CONDUCTION; WAVELET METHOD;
D O I
10.1007/s10910-012-0036-4
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In this paper a numerical method consists of combining Haar basis method and Tikhonov regularization method for solving some ill-posed inverse problems using noisy data is presented. By using a sensor located at a point inside the body and measuring the u(x, t) at a point x = a, 0 < a < 1, and applying Haar basis method to the inverse problem, we determine a stable numerical solution to this problem. Results show that an excellent estimation on the unknown functions of the inverse problem can be obtained within a couple of minutes CPU time at pentium IV-2.4 GHz PC.
引用
收藏
页码:2317 / 2337
页数:21
相关论文
共 50 条
  • [21] Minimax goodness-of-fit testing in ill-posed inverse problems with partially unknown operators
    Marteau, Clement
    Sapatinas, Theofanis
    ANNALES DE L INSTITUT HENRI POINCARE-PROBABILITES ET STATISTIQUES, 2017, 53 (04): : 1675 - 1718
  • [22] Optimally scaled vector regularization method to solve ill-posed linear problems
    Liu, Chein-Shan
    APPLIED MATHEMATICS AND COMPUTATION, 2012, 218 (21) : 10602 - 10616
  • [23] TWO NEW NON-NEGATIVITY PRESERVING ITERATIVE REGULARIZATION METHODS FOR ILL-POSED INVERSE PROBLEMS
    Zhang, Ye
    Hofmann, Bernd
    INVERSE PROBLEMS AND IMAGING, 2021, 15 (02) : 229 - 256
  • [24] Data-driven solutions of ill-posed inverse problems arising from doping reconstruction in semiconductors
    Piani, S.
    Farrell, P.
    Lei, W.
    Rotundo, N.
    Heltai, L.
    APPLIED MATHEMATICS IN SCIENCE AND ENGINEERING, 2024, 32 (01):
  • [25] Bias-corrected regularized solution to inverse ill-posed models
    Shen, Yunzhong
    Xu, Peiliang
    Li, Bofeng
    JOURNAL OF GEODESY, 2012, 86 (08) : 597 - 608
  • [26] Comparing parameter choice methods for regularization of ill-posed problems
    Bauer, Frank
    Lukas, Mark A.
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2011, 81 (09) : 1795 - 1841
  • [27] Old and new parameter choice rules for discrete ill-posed problems
    Reichel, Lothar
    Rodriguez, Giuseppe
    NUMERICAL ALGORITHMS, 2013, 63 (01) : 65 - 87
  • [28] Determination of singular value truncation threshold for regularization in ill-posed problems
    Duan, Shuyong
    Yang, Botao
    Wang, Fang
    Liu, Guirong
    INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, 2021, 29 (08) : 1127 - 1157
  • [29] A special modified Tikhonov regularization matrix for discrete ill-posed problems
    Cui, Jingjing
    Peng, Guohua
    Lu, Quan
    Huang, Zhengge
    APPLIED MATHEMATICS AND COMPUTATION, 2020, 377
  • [30] RISK ESTIMATORS FOR CHOOSING REGULARIZATION PARAMETERS IN ILL-POSED PROBLEMS - PROPERTIES AND LIMITATIONS
    Lucka, Felix
    Proksch, Tharina
    Brune, Christoph
    Bissantz, Nicolai
    Burger, Martin
    Dette, Holger
    Wubbeeling, Frank
    INVERSE PROBLEMS AND IMAGING, 2018, 12 (05) : 1121 - 1155