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Description
  • Vrstvené umělé neuronové sítě jsou použitelné v mnoha technických a vědeckých odvětvích. Např. modelování a simulace. Před tím než začneme navrhovat neuronovou síť a používat ji k řešení konkrétních situací, musíme být schopni naučit daný typ sítě na daný problém. Během tohoto učebního procesu může nastat mnoho situací, které mohou komplikovat cestu k úspěšnému řešení. Proto je v tomto článku vybrán učicí algoritmus Back Propagation na, kterém jsou demonstrovány některé tyto situace. (cs)
  • Artificial multilayer feed-forward neural networks are useful in many technical and scientific branches. For example, one of them is modeling and simulation. Before use any designed artificial neural network, we have to teach it for our task. The teaching process is also sometimes called training of neural network. During this teaching process some of many parameters of artificial neural network are adapted by data, which represent our task. For teaching process we can apply some special algorithms, like Back Propagation. However sometimes, during teaching process we can see situations, when is impossible stop teaching process, because error of teaching process is still %22so big%22. The neural network can't adapt own parameters by all data, which represent our task. This paper describe some reasons of errors arise during the teaching process and also describe methods to eliminate some of these errors.
  • Artificial multilayer feed-forward neural networks are useful in many technical and scientific branches. For example, one of them is modeling and simulation. Before use any designed artificial neural network, we have to teach it for our task. The teaching process is also sometimes called training of neural network. During this teaching process some of many parameters of artificial neural network are adapted by data, which represent our task. For teaching process we can apply some special algorithms, like Back Propagation. However sometimes, during teaching process we can see situations, when is impossible stop teaching process, because error of teaching process is still %22so big%22. The neural network can't adapt own parameters by all data, which represent our task. This paper describe some reasons of errors arise during the teaching process and also describe methods to eliminate some of these errors. (en)
Title
  • Training Processes of Artificial Multilayer Neural Networks
  • Učební metody umělých vrstvených neuronových sítí (cs)
  • Training Processes of Artificial Multilayer Neural Networks (en)
skos:prefLabel
  • Training Processes of Artificial Multilayer Neural Networks
  • Učební metody umělých vrstvených neuronových sítí (cs)
  • Training Processes of Artificial Multilayer Neural Networks (en)
skos:notation
  • RIV/61989100:27350/06:00014526!RIV07-MSM-27350___
http://linked.open.../vavai/riv/strany
  • 487-494
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
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http://linked.open...aciTvurceVysledku
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  • 504142
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  • RIV/61989100:27350/06:00014526
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  • Training algorithms; Artificial neural network; BackPropagation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [510A5ADA08E1]
http://linked.open...i/riv/mistoVydani
  • Fukuoka
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  • 4th International Workshop on Earth Science and Technology
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http://linked.open...cetTvurcuVysledku
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http://linked.open...iv/tvurceVysledku
  • Řepka, Michal
number of pages
http://purl.org/ne...btex#hasPublisher
  • Kyushu Univerzity
https://schema.org/isbn
  • 978-4-9902356-7-3
http://localhost/t...ganizacniJednotka
  • 27350
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