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Description
  • The paper is devoted to the comparison of prediction abilities of classical multi-input and multi-output (MIMO) models. The main part presents results gained from different types of neural networks compared with other approaches to data prediction. Proposed algorithms were verified for simulated signals at first and then used for real data representing gas consumption in the Czech Republic.
  • The paper is devoted to the comparison of prediction abilities of classical multi-input and multi-output (MIMO) models. The main part presents results gained from different types of neural networks compared with other approaches to data prediction. Proposed algorithms were verified for simulated signals at first and then used for real data representing gas consumption in the Czech Republic. (en)
  • Příspěvek je zaměřen na porovnání možností predikce signálů s pomocí klasických modelů s více vstupy a výstupy (MIMO). Hlavní část studie presentuje výsledky získané různými typy neuronových sítí a dalšími metodami predikce. Navržené algoritmy byly ověřeny na simulovaných datech a dále použity pro zpracování reálných dat spotřeby plynu v České republice. (cs)
Title
  • MIMO modely a kombinace neuronových sítí v predikci časových řad spotřeby plynu (cs)
  • MIMO Models and Combinations of Neural Networks in Prediction of Gas Consumption
  • MIMO Models and Combinations of Neural Networks in Prediction of Gas Consumption (en)
skos:prefLabel
  • MIMO modely a kombinace neuronových sítí v predikci časových řad spotřeby plynu (cs)
  • MIMO Models and Combinations of Neural Networks in Prediction of Gas Consumption
  • MIMO Models and Combinations of Neural Networks in Prediction of Gas Consumption (en)
skos:notation
  • RIV/60461373:22340/04:00011185!RIV/2005/MSM/223405/N
http://linked.open.../vavai/riv/strany
  • R065/1-R065/13
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 223400007)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 573675
http://linked.open...ai/riv/idVysledku
  • RIV/60461373:22340/04:00011185
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Linear neural network;feed forward neural network;recurrent neural network;Elman neural network;prediction;gas consumption;parametric model (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [92E83E8BA84D]
http://linked.open...v/mistoKonaniAkce
  • Kouty nad Desnou
http://linked.open...i/riv/mistoVydani
  • Pardubice
http://linked.open...i/riv/nazevZdroje
  • Proc. of the 6th International Scientific-Technical Conference Process Control 2004
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Procházka, Aleš
  • Pavelka, Aleš
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Univerzita Pardubice
https://schema.org/isbn
  • 80-7194-662-1
http://localhost/t...ganizacniJednotka
  • 22340
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