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Wyszukujesz frazę "regularization" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
On convergence of regularization methods for nonlinear parabolic optimal control problems with control and state constraints
Autorzy:
Neitzel, I.
Troltzsch, F.
Powiązania:
https://bibliotekanauki.pl/articles/970314.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
optimal control
semilinear parabolic equation
pointwise state constraints
Moreau-Yosida regularization
Lavrentiev regularization
convergence
strong regularity
local uniqueness
Opis:
Moreau-Yosida and Lavrentiev type regularization methods are considered for nonlinear optimal control problems governed by semilinear parabolic equations with bilateral pointwise control and state constraints. The convergence of optimal controls of the regularized problems is studied for regularization parameters tending to infinity or zero, respectively. In particular, the strong convergence of global and local solutions is addressed. Moreover, strong regularity of the Lavrentiev-regularized optimality system is shown under certain assumptions, which, in particular, allows to show that locally optimal solutions of the Lavrentiev regularized problems are locally unique. This analysis is based on a second-order sufficient optimality condition and a separation assumption on almost active sets.
Źródło:
Control and Cybernetics; 2008, 37, 4; 1013-1043
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Convergence Analysis of Inverse Iterative Neural Networks with L₂ Penalty
Autorzy:
Wen, Y.
Wang, J.
Huang, B.
Zurada, J. M.
Powiązania:
https://bibliotekanauki.pl/articles/108754.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi
Tematy:
neural networks
gradient descent
inverse iterative
monotonicity
regularization
convergence
Opis:
The iterative inversion of neural networks has been used in solving problems of adaptive control due to its good performance of information processing. In this paper an iterative inversion neural network with L₂ penalty term has been presented trained by using the classical gradient descent method. We mainly focus on the theoretical analysis of this proposed algorithm such as monotonicity of error function, boundedness of input sequences and weak (strong) convergence behavior. For bounded property of inputs, we rigorously proved that the feasible solutions of input are restricted in a measurable field. The weak convergence means that the gradient of error function with respect to input tends to zero as the iterations go to infinity while the strong convergence stands for the iterative sequence of input vectors convergence to a fixed optimal point.
Źródło:
Journal of Applied Computer Science Methods; 2016, 8 No. 2; 85-98
1689-9636
Pojawia się w:
Journal of Applied Computer Science Methods
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Regularization of non-coercive quasi variational inequalities
Autorzy:
Giannessi, F.
Khan, A.
Powiązania:
https://bibliotekanauki.pl/articles/205586.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
operator monotoniczny i pseudomonotoniczny
zbieżność
convergence
monotone and pseudo-monotone operators
non-coercive
quasi-variational inequalities
regularization
Opis:
This paper is devoted to the regularization of quasi-variational inequalities. The quasi-variational inequality is considered with multivalued operator. The operator involved is taken to be non-coercive and the data are assumed to be known approximately only. Under the assumption that the quasi-variational inequality be solvable, a weakly convergent approximation procedure is designed by means of the so-called Browder-Tikhonov regularization method.
Źródło:
Control and Cybernetics; 2000, 29, 1; 91-110
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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