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  • Článek popisuje a porovnává aktuální techniky pro simulaci umělých neuronových sítí. Zaměřuje se na simulátory, které umožňují snadný návrh nových neuronových sítí. Při simulaci moderních neuronových sítí je využíváno několika strategií. Nejvíce efektivní simulační strategií je synchronní simulace. Článek zmiňuje příklady obecných systémů pro simulace, které mohou být též využity pro simualci neuronových sítí. Běžné neuronové simulátory často závisí na typu simulované sítě, univerzální simulátory jsou zase přiliš obecné, ale zato podporují přirozené propojování simulovaných jednotek - neuronů v tomto případě. (cs)
  • In this paper actual simulation techniques and simulation systems for artificial neural networks are compared. We focus on neural network simulators that allow a user easy design of new neural networks. There are several simulation strategies that can be exploited by modern neural network simulators described. We considered the synchronous simulation as the most effective for parallel systems like artificial neural networks. Examples of general simulation systems that can be used for simulation of neural networks are mentioned. Current neural network simulators commonly depend on a type of neural network simulated and cannot be easily extended to simulate a different or a neural network with a brand new architecture and function. Universal simulation tools seem to be suitable for network design but do not support connectionism natively.
  • In this paper actual simulation techniques and simulation systems for artificial neural networks are compared. We focus on neural network simulators that allow a user easy design of new neural networks. There are several simulation strategies that can be exploited by modern neural network simulators described. We considered the synchronous simulation as the most effective for parallel systems like artificial neural networks. Examples of general simulation systems that can be used for simulation of neural networks are mentioned. Current neural network simulators commonly depend on a type of neural network simulated and cannot be easily extended to simulate a different or a neural network with a brand new architecture and function. Universal simulation tools seem to be suitable for network design but do not support connectionism natively. (en)
Title
  • New Trends in Simulation of Neural Networks
  • New Trends in Simulation of Neural Networks (en)
  • Nové trendy v simulaci neuronových sítí (cs)
skos:prefLabel
  • New Trends in Simulation of Neural Networks
  • New Trends in Simulation of Neural Networks (en)
  • Nové trendy v simulaci neuronových sítí (cs)
skos:notation
  • RIV/68407700:21230/07:03132655!RIV08-MSM-21230___
http://linked.open.../vavai/riv/strany
  • Nečíslováno
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  • Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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http://linked.open...iv/duvernostUdaju
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  • 437250
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  • RIV/68407700:21230/07:03132655
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  • neural networks; prigramming language; simulation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [332258E9D14E]
http://linked.open...v/mistoKonaniAkce
  • Ljubljana
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  • Vienna
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 6th EUROSIM Congress on Modelling and Simulation
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Koutník, Jan
  • Šnorek, Miroslav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • ARGESIM
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  • 978-3-901608-32-2
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  • 21230
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