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  • In this paper some remarks on predictive modeling of traction power consumption and their use in intelligent control systems are stated. Special emphasis is put on discussing neural networks and genetic algorithms for such models described in the second chapter. In the third chapter, significant applications of neural networks and genetic algorithms in area of power consumption and train diagram are stated. A methodology of model development and assessment is presented in the chapter 4. In the chapter 5 there are up to now results of author's traction power consumption prediction coming out from artificial neural network predictive models developed in Mathematica(C) SW environment. Finally, summary and further work are stated in the last chapter.
  • In this paper some remarks on predictive modeling of traction power consumption and their use in intelligent control systems are stated. Special emphasis is put on discussing neural networks and genetic algorithms for such models described in the second chapter. In the third chapter, significant applications of neural networks and genetic algorithms in area of power consumption and train diagram are stated. A methodology of model development and assessment is presented in the chapter 4. In the chapter 5 there are up to now results of author's traction power consumption prediction coming out from artificial neural network predictive models developed in Mathematica(C) SW environment. Finally, summary and further work are stated in the last chapter. (en)
  • V tomto článku jsou uvedeny některé poznámky o prediktivním modelování odběrů trakčního výkonu a jeho použití v inteligentních řídicích systémech. Zvláštní důraz je kladen na uvažování neuronových sítí a genetických algoritmů pro tyto modely (kapitola 2). Ve třetí kapitole jsou popsány důležité aplikace neuronových sítí a genetických algoritmů v oblasti odběrů elektrického výkonu a grafikonu vlakové dopravy. V kapitole 4 je uvedena metodologie vývoje a hodnocení modelu. V 5. kapitole jsou prezentovány dosavadní výsledky autorových predikcí na bázi umělých neuronových sítí vyvinutých v SW prostředí Mathematica(C). V poslední kapitole je uvedeno shrnutí a výhled další práce. (cs)
Title
  • Artificial intelligence methods used for predicting traction power consumption
  • Artificial intelligence methods used for predicting traction power consumption (en)
  • Použití metod umělé inteligence pro predikování odběrů trakčního výkonu (cs)
skos:prefLabel
  • Artificial intelligence methods used for predicting traction power consumption
  • Artificial intelligence methods used for predicting traction power consumption (en)
  • Použití metod umělé inteligence pro predikování odběrů trakčního výkonu (cs)
skos:notation
  • RIV/68407700:21260/08:06149256!RIV09-GA0-21260___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GD102/05/H517)
http://linked.open...iv/cisloPeriodika
  • 1
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
  • 357048
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21260/08:06149256
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Neural networks; Prediction; Traction power consumption (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [9A97AE3CFDBA]
http://linked.open...i/riv/nazevZdroje
  • Neural Network World
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 2008
http://linked.open...iv/tvurceVysledku
  • Sadil, Jindřich
http://linked.open...ain/vavai/riv/wos
  • 000254356900003
issn
  • 1210-0552
number of pages
http://localhost/t...ganizacniJednotka
  • 21260
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