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Wyszukujesz frazę "regularization" wg kryterium: Wszystkie pola


Wyświetlanie 1-3 z 3
Tytuł:
Performance analysis of regularization algorithms used for image reconstruction in computed tomography
Autorzy:
Wysoczański, D.
Mroczka, J.
Polak, A. G.
Powiązania:
https://bibliotekanauki.pl/articles/201356.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
computed tomography
image reconstruction
algebraic approach
regularization methods
Opis:
Computed tomography is one of the most significant diagnostic techniques in medicine. This work is focused on hard-field imaging, where signals take a form of straight rays and the reconstructed image can be presented as a matrix with unknown pixels. Algebraic methods for direct computation of the image have not been used in practice because of the scale of the problem and numerical errors appearing in the solution. The aim of this work was to analyse the performance of direct algebraic algorithms for tomographic image reconstruction including regularisation mechanism such as: generalised regularisation, Tikhonov regularisation, Twomey regularisation and ridge regression (RR), as well as comparing the results with the filtered backprojection (FBP) as the reference method. The performed analyses demonstrated that the regularised algebraic methods are more accurate than the commonly used FBP, and RR appeared the most precise among them. Additionally it was shown that the invariant system matrix (inverted during calculations) can be easily determined by solving the forward problem. Finally, potential directions of further research have been pointed out.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 2; 467-474
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimisation of neural state variables estimators of two-mass drive system using the Bayesian regularization method
Autorzy:
Kamiński, M.
Orłowska-Kowalska, T.
Powiązania:
https://bibliotekanauki.pl/articles/202379.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electrical drive
two-mass system
state estimation
neural networks
training methods
Bayesian regularization
Opis:
The paper deals with the application of neural networks for state variables estimation of the electrical drive system with an elastic joint. The torsional vibration suppression of such drive system is achieved by the application of a special control structure with a state-space controller and additional feedbacks from mechanical state variables. Signals of the torsional torque and the load-machine speed, estimated by neural networks are used in the control structure. In the learning procedure of the neural networks a modified objective function with the regularization technique is introduced. For choosing the regularization parameters, the Bayesian interpretation of neural networks is used. It gives a possibility to calculate automatically these parameters in the learning process. In this work results obtained with the classical Levenberg-Marquardt algorithm and the expanded one by a regularization function are compared. High accuracy of the reconstructed signals is obtained without the necessity of the electrical drive system parameters identification. Simulation results show good precision of both presented neural estimators for a wide range of changes of the load speed and torque. Simulation results are verified by the laboratory experiments.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2011, 59, 1; 33-38
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Generalized ordered linear regression with regularization
Autorzy:
Łęski, J.
Henzel, N.
Powiązania:
https://bibliotekanauki.pl/articles/201591.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
linear regression
IRLS
OWA
conjugate gradient optimization
robust methods
Opis:
Linear regression analysis has become a fundamental tool in experimental sciences. We propose a new method for parameter estimation in linear models. The 'Generalized Ordered Linear Regression with Regularization' (GOLRR) uses various loss functions (including the o-insensitive ones), ordered weighted averaging of the residuals, and regularization. The algorithm consists in solving a sequence of weighted quadratic minimization problems where the weights used for the next iteration depend not only on the values but also on the order of the model residuals obtained for the current iteration. Such regression problem may be transformed into the iterative reweighted least squares scenario. The conjugate gradient algorithm is used to minimize the proposed criterion function. Finally, numerical examples are given to demonstrate the validity of the method proposed.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 481-489
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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