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Description
  • It is the purpose of the present paper to suggest an approach to utilization of mathematical models of a classification system for pattern recognition. Pattern recognition has a long history but has recently become much more widespread as the automated capture of signals and images has become cheaper. Very many of the applications of neural networks are to classification, and so are within the field of pattern recognition. This article describes a classification system for pattern recognition based on artificial neural networks with modular architecture. We use a three layer feedforward network model that is learned with the backpropagation algorithm for all experiments. Our experimental recognition objects were digits and their type fonts. We also propose outline further development on this topic in conclusion.
  • It is the purpose of the present paper to suggest an approach to utilization of mathematical models of a classification system for pattern recognition. Pattern recognition has a long history but has recently become much more widespread as the automated capture of signals and images has become cheaper. Very many of the applications of neural networks are to classification, and so are within the field of pattern recognition. This article describes a classification system for pattern recognition based on artificial neural networks with modular architecture. We use a three layer feedforward network model that is learned with the backpropagation algorithm for all experiments. Our experimental recognition objects were digits and their type fonts. We also propose outline further development on this topic in conclusion. (en)
Title
  • Simulation of pattern recognition system via modular neural networks
  • Simulation of pattern recognition system via modular neural networks (en)
skos:prefLabel
  • Simulation of pattern recognition system via modular neural networks
  • Simulation of pattern recognition system via modular neural networks (en)
skos:notation
  • RIV/61988987:17310/11:A13014MN!RIV13-MSM-17310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
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
  • 229461
http://linked.open...ai/riv/idVysledku
  • RIV/61988987:17310/11:A13014MN
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Simulation; pattern recognition; modular neural networks; neuro-classifier (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • SK - Slovenská republika
http://linked.open...ontrolniKodProRIV
  • [D1C0ABE9C384]
http://linked.open...i/riv/nazevZdroje
  • Aplimat - Journal of Applied Mathematics
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 4
http://linked.open...iv/tvurceVysledku
  • Janošek, Michal
  • Volná, Eva
  • Kotyrba, Martin
  • Kocian, Václav
issn
  • 1337-6365
number of pages
http://localhost/t...ganizacniJednotka
  • 17310
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