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  • This paper describes application of Genetic algorithm (GA) for design of network configuration and for learning of neural network. Design of network configuration is the first area for GA exercise in relation to neural network. The number of neurons in network and placement to the layers has big influence over effectivity of whole system. If we are able to formulate quality classification of designed network from standpoint of topology, we can use GA for design of suitable network configuration. The second area (learning of neural network) consists in using of advantages of GA toward learning of neural networks. In this case GA looks for acceptable setting of network weights so, to make specified transformation - it practices minimalization of its mistake function. The Genetic algorithm is considered to be a stochastic heuristic (or meta-heuristic) method. Genetic algorithms are inspired by adaptive and evolutionary mechanisms of live organisms. The best use of Genetic algorithm can be found in solvin
  • This paper describes application of Genetic algorithm (GA) for design of network configuration and for learning of neural network. Design of network configuration is the first area for GA exercise in relation to neural network. The number of neurons in network and placement to the layers has big influence over effectivity of whole system. If we are able to formulate quality classification of designed network from standpoint of topology, we can use GA for design of suitable network configuration. The second area (learning of neural network) consists in using of advantages of GA toward learning of neural networks. In this case GA looks for acceptable setting of network weights so, to make specified transformation - it practices minimalization of its mistake function. The Genetic algorithm is considered to be a stochastic heuristic (or meta-heuristic) method. Genetic algorithms are inspired by adaptive and evolutionary mechanisms of live organisms. The best use of Genetic algorithm can be found in solvin (en)
Title
  • Genetic Algorithm and Neural Network
  • Genetic Algorithm and Neural Network (en)
skos:prefLabel
  • Genetic Algorithm and Neural Network
  • Genetic Algorithm and Neural Network (en)
skos:notation
  • RIV/00216305:26220/07:PU70349!RIV11-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(2E06034), P(GA102/07/1503), Z(MSM0021630513), Z(MSM0021630529)
http://linked.open...iv/cisloPeriodika
  • 3
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
  • 423109
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/07:PU70349
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Genetic algorithm, fitness function, neural network, back-propagation method. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GR - Řecká republika
http://linked.open...ontrolniKodProRIV
  • [887E6A8AB48D]
http://linked.open...i/riv/nazevZdroje
  • WSEAS Applied Informatics & Communications
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
  • 2007
http://linked.open...iv/tvurceVysledku
  • Škorpil, Vladislav
  • Šťastný, Jiří
http://linked.open...ain/vavai/riv/wos
  • 000257280400060
http://linked.open...n/vavai/riv/zamer
issn
  • 1790-5117
number of pages
http://localhost/t...ganizacniJednotka
  • 26220
is http://linked.open...avai/riv/vysledek of
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