Regularization parameter estimation for large-scale Tikhonov regularization using a priori information

2010 ◽  
Vol 54 (12) ◽  
pp. 3430-3445 ◽  
Author(s):  
Rosemary A. Renaut ◽  
Iveta Hnětynková ◽  
Jodi Mead
1993 ◽  
Vol 115 (1) ◽  
pp. 7-11
Author(s):  
Min-Shin Chen

A new decentralized controller is proposed for a group of subsystems subject to unknown interconnections and external disturbances. Under the assumption that the interconnections and disturbances satisfy a certain structural condition, the new controller suppresses the disturbances and intercoupling effects completely, making the overall system behave as a decoupled system. In this new control design, each local controller uses an uncertainty estimator proposed by Chen (1990) for estimation of the interconnections and disturbances, and then cancels these undesirable inputs directly. The major advantages of the proposed controller are that the interconnections need not satisfy the so-called “conical” condition, and there is no need for a priori information on the magnitudes of interconnections and disturbances.


Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 982
Author(s):  
Marta Gatto ◽  
Fabio Marcuzzi

In this paper we analyze the bias in a general linear least-squares parameter estimation problem, when it is caused by deterministic variables that have not been included in the model. We propose a method to substantially reduce this bias, under the hypothesis that some a-priori information on the magnitude of the modelled and unmodelled components of the model is known. We call this method Unbiased Least-Squares (ULS) parameter estimation and present here its essential properties and some numerical results on an applied example.


2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Stefan Diebels ◽  
Tobias Scheffer ◽  
Thomas Schuster ◽  
Aaron Wewior

For studying the interaction of displacements, stresses, and acting forces for elastic and viscoelastic materials, it is of utmost importance to have a decent mathematical model available. Usually such a model consists of a coupled set of nonlinear differential equations together with appropriate boundary conditions. However, since the different material classes vary significantly with respect to their physical and mechanical behavior, the parameters which appear in these equations are unknown and therefore have to be determined before the equations can be used for further investigations or simulations. It is this very step which is addressed in this article where we consider elastic as well as viscoelastic material behavior. The idea is to compute the parameters as solutions of a minimization problem for Tikhonov functionals. Tikhonov regularization is a well-established solution technique for tackling inverse problems. On the one hand, it assures a computation that is stable with respect to noisy input data, and on the other hand, it involves desired a priori information on the solution. In this article we develop problem adapted Tikhonov functionals and prove that a Tikhonov regularization improves the accuracy especially when the underlying system is ill-conditioned.


2021 ◽  
Vol 7 (2) ◽  
pp. 18
Author(s):  
Germana Landi ◽  
Fabiana Zama ◽  
Villiam Bortolotti

This paper is concerned with the reconstruction of relaxation time distributions in Nuclear Magnetic Resonance (NMR) relaxometry. This is a large-scale and ill-posed inverse problem with many potential applications in biology, medicine, chemistry, and other disciplines. However, the large amount of data and the consequently long inversion times, together with the high sensitivity of the solution to the value of the regularization parameter, still represent a major issue in the applicability of the NMR relaxometry. We present a method for two-dimensional data inversion (2DNMR) which combines Truncated Singular Value Decomposition and Tikhonov regularization in order to accelerate the inversion time and to reduce the sensitivity to the value of the regularization parameter. The Discrete Picard condition is used to jointly select the SVD truncation and Tikhonov regularization parameters. We evaluate the performance of the proposed method on both simulated and real NMR measurements.


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