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Description
  • Artificial neural networks have the ability to model signals and predict values that are complex and for which an explicit representation is not known. Therefore, it is a convenient method for modeling of electroencephalograph due to the easiness of acquiring large data sets and a great difficulty to interpret the signals. The growth of computational power in connection with the intensification of the knowledge of biological neural networks open new possibilities for signal modelling.
  • Artificial neural networks have the ability to model signals and predict values that are complex and for which an explicit representation is not known. Therefore, it is a convenient method for modeling of electroencephalograph due to the easiness of acquiring large data sets and a great difficulty to interpret the signals. The growth of computational power in connection with the intensification of the knowledge of biological neural networks open new possibilities for signal modelling. (en)
Title
  • Modeling of EEG Signal with Homeostatic Neural Network
  • Modeling of EEG Signal with Homeostatic Neural Network (en)
skos:prefLabel
  • Modeling of EEG Signal with Homeostatic Neural Network
  • Modeling of EEG Signal with Homeostatic Neural Network (en)
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  • RIV/68407700:21260/13:00226437!RIV15-MSM-21260___
http://linked.open...avai/riv/aktivita
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  • S
http://linked.open...vai/riv/dodaniDat
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  • 88865
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  • RIV/68407700:21260/13:00226437
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  • EEG; neural network; signal modeling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A3ABBBED25E2]
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  • Ostrava
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems
http://linked.open...in/vavai/riv/obor
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  • Růžek, Martin
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http://linked.open.../riv/zahajeniAkce
issn
  • 2194-5357
number of pages
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  • Springer-Verlag
https://schema.org/isbn
  • 978-3-319-00541-6
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  • 21260
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