Object
Title: Stochastic neural networks for feasibility checking
Contributor:
Korbicz, Józef - red. ; Uciński, Dariusz - red.
Group publication title:
Abstract:
Complex diagnosis problems, defined by high-level models, often lead to constraint-based discrete optimization tasks. A logical description of large, complex systems usually contains numerous variables. The first test of the logical description is typically to check the feasibility in order to know that there is no contradiction in the model. ; This can be formulated as an optimization problem and methods of discrete optimization theory can then be used. The purpose of the paper is to show that stochastic neural networks can be applied to this type of tasks and the networks are efficient tools for finding feasible or good-quality configurations. ; Boltzmann and mean-field neural networks were tested on large-sized complex problems. The paper presents simulation results obtained from a real application task and compares the performance of the neural networks being examined.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
Pages:
Source:
AMCS, volume 9, number 4 (1999) ; click here to follow the link
Language:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Digital Library of Zielona Góra > Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Digital Library of Zielona Góra > Repository > Types of work > Articles
- Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS)
- Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (1999)
Last modified:
Sep 3, 2021
In our library since:
Mar 11, 2021
Number of object content hits:
37
All available object's versions:
https://www.zbc.uz.zgora.pl/publication/64917
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International Journal of Applied Mathematics and Computer Science (AMCS)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 23 (2013)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 22 (2012)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 21 (2011)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 20 (2010)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 19 (2009)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 18 (2008)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 17 (2007)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 16 (2006)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 15 (2005)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 13 (2003)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 12 (2002)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 11 (2001)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 10 (2000)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 1 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 2 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 3 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 9, number 4 (1999)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 23 (2013)
| Edition name | Date |
|---|---|
| Stochastic neural networks for feasibility checking | Sep 3, 2021 |
