"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." . "1210-0552" . "21260" . "Neural Network World" . . . . "Sadil, Jind\u0159ich" . "1" . "2008" . "P(GD102/05/H517)" . . "[9A97AE3CFDBA]" . . "Neural networks; Prediction; Traction power consumption"@en . . "RIV/68407700:21260/08:06149256" . . "Pou\u017Eit\u00ED metod um\u011Bl\u00E9 inteligence pro predikov\u00E1n\u00ED odb\u011Br\u016F trak\u010Dn\u00EDho v\u00FDkonu"@cs . . . . "8"^^ . "Artificial intelligence methods used for predicting traction power consumption" . . "CZ - \u010Cesk\u00E1 republika" . "RIV/68407700:21260/08:06149256!RIV09-GA0-21260___" . . "Artificial intelligence methods used for predicting traction power consumption"@en . . "357048" . "1"^^ . "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 . "Artificial intelligence methods used for predicting traction power consumption"@en . "Pou\u017Eit\u00ED metod um\u011Bl\u00E9 inteligence pro predikov\u00E1n\u00ED odb\u011Br\u016F trak\u010Dn\u00EDho v\u00FDkonu"@cs . . "1"^^ . . "V tomto \u010Dl\u00E1nku jsou uvedeny n\u011Bkter\u00E9 pozn\u00E1mky o prediktivn\u00EDm modelov\u00E1n\u00ED odb\u011Br\u016F trak\u010Dn\u00EDho v\u00FDkonu a jeho pou\u017Eit\u00ED v inteligentn\u00EDch \u0159\u00EDdic\u00EDch syst\u00E9mech. Zvl\u00E1\u0161tn\u00ED d\u016Fraz je kladen na uva\u017Eov\u00E1n\u00ED neuronov\u00FDch s\u00EDt\u00ED a genetick\u00FDch algoritm\u016F pro tyto modely (kapitola 2). Ve t\u0159et\u00ED kapitole jsou pops\u00E1ny d\u016Fle\u017Eit\u00E9 aplikace neuronov\u00FDch s\u00EDt\u00ED a genetick\u00FDch algoritm\u016F v oblasti odb\u011Br\u016F elektrick\u00E9ho v\u00FDkonu a grafikonu vlakov\u00E9 dopravy. V kapitole 4 je uvedena metodologie v\u00FDvoje a hodnocen\u00ED modelu. V 5. kapitole jsou prezentov\u00E1ny dosavadn\u00ED v\u00FDsledky autorov\u00FDch predikc\u00ED na b\u00E1zi um\u011Bl\u00FDch neuronov\u00FDch s\u00EDt\u00ED vyvinut\u00FDch v SW prost\u0159ed\u00ED Mathematica(C). V posledn\u00ED kapitole je uvedeno shrnut\u00ED a v\u00FDhled dal\u0161\u00ED pr\u00E1ce."@cs . "000254356900003" . "Artificial intelligence methods used for predicting traction power consumption" .