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  • Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems based on neural networks and fuzzy logic. Therefore, the paper presents a design of a model for air quality assessment based on a combination of Kohonen's self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localities into classes assessing their air quality.
  • Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems based on neural networks and fuzzy logic. Therefore, the paper presents a design of a model for air quality assessment based on a combination of Kohonen's self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localities into classes assessing their air quality. (en)
Title
  • Air quality assessment using neural networks and fuzzy logic
  • Air quality assessment using neural networks and fuzzy logic (en)
skos:prefLabel
  • Air quality assessment using neural networks and fuzzy logic
  • Air quality assessment using neural networks and fuzzy logic (en)
skos:notation
  • RIV/00216275:25410/11:39892224!RIV12-MZP-25410___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
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  • P(SP/4I2/60/07)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
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  • 184915
http://linked.open...ai/riv/idVysledku
  • RIV/00216275:25410/11:39892224
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Neuro-fuzzy systems; Neural networks; Fuzzy logic; Assessment; Air quality (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [762BD606D3F1]
http://linked.open...v/mistoKonaniAkce
  • Iasi
http://linked.open...i/riv/mistoVydani
  • Atény
http://linked.open...i/riv/nazevZdroje
  • Recent Researches in Environment, Energy Planning and Pollution
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...iv/tvurceVysledku
  • Hájek, Petr
  • Olej, Vladimír
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • WSEAS Press
https://schema.org/isbn
  • 978-1-61804-012-1
http://localhost/t...ganizacniJednotka
  • 25410
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