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Wyszukujesz frazę "optimal experimental design" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Construction of constrained experimental designs on finite spaces for a modified Ek-optimality criterion
Autorzy:
Uciński, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/1838172.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
constrained optimum experimental design
minimal sum of largest eigenvalues
generalized simplicial decomposition
optimal measurement selection
Opis:
A simple computational algorithm is proposed for minimizing sums of largest eigenvalues of the matrix inverse over the set of all convex combinations of a finite number of nonnegative definite matrices subject to additional box constraints on the weights of those combinations. Such problems arise when experimental designs aiming at minimizing sums of largest asymptotic variances of the least-squares estimators are sought and the design region consists of finitely many support points, subject to the additional constraints that the corresponding design weights are to remain within certain limits. The underlying idea is to apply the method of outer approximations for solving the associated convex semi-infinite programming problem, which reduces to solving a sequence of finite min-max problems. A key novelty here is that solutions to the latter are found using generalized simplicial decomposition, which is a recent extension of the classical simplicial decomposition to nondifferentiable optimization. Thereby, the dimensionality of the design problem is drastically reduced. The use of the algorithm is illustrated by an example involving optimal sensor node activation in a large sensor network collecting measurements for parameter estimation of a spatiotemporal process.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2020, 30, 4; 659-677
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimal training strategies for locally recurrent neural networks
Autorzy:
Patan, K.
Patan, M.
Powiązania:
https://bibliotekanauki.pl/articles/1396735.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
training schedule
neural network
Fisher information matrix
network parameters
optimal experimental design
convex optimization theory
Opis:
The problem of determining an optimal training schedule for locally recurrent neural network is discussed. Specifically, the proper choice of the most informative measurement data guaranteeing the reliable prediction of neural network response is considered. Based on a scalar measure of performance defined on the Fisher information matrix related to the network parameters, the problem was formulated in terms of optimal experimental design. Then, its solution can be readily achieved via adaptation of effective numerical algorithms based on the convex optimization theory. Finally, some illustrative experiments are provided to verify the presented approach.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 2; 103-114
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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