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Statements

Subject Item
n2:RIV%2F70883521%3A28140%2F09%3A63508011%21RIV10-GA0-28140___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
The paper deals with a promising approach of modeling the real life systems, characterized with sets of measured/discrete data, by replacing them with analytical functions framework. the article is focused on neural network approximation of functional expressions. As an analyzed system a dynamic flight model has been chosen due to the necessity of considering several classes of large sets of aerodynamic lift, drag, speed, force, balance and mass data to get a comparable mock-up response. Handling such type of model is naturally a huge computation time demanding process. Being able to substitute it with analytical functions system presenting a coincident behaviour could dramatically improve computation time at all aspects of utilization (UAV/UAS, autopilot systems, flight simulators, real time control & stability response determination, e.t.c). Therefore first steps how to obtain analytical function are shown here. In this paper, sample case parameters were used to produce data that were then fitte The paper deals with a promising approach of modeling the real life systems, characterized with sets of measured/discrete data, by replacing them with analytical functions framework. the article is focused on neural network approximation of functional expressions. As an analyzed system a dynamic flight model has been chosen due to the necessity of considering several classes of large sets of aerodynamic lift, drag, speed, force, balance and mass data to get a comparable mock-up response. Handling such type of model is naturally a huge computation time demanding process. Being able to substitute it with analytical functions system presenting a coincident behaviour could dramatically improve computation time at all aspects of utilization (UAV/UAS, autopilot systems, flight simulators, real time control & stability response determination, e.t.c). Therefore first steps how to obtain analytical function are shown here. In this paper, sample case parameters were used to produce data that were then fitte
dcterms:title
Neural Differentiation in Modeling Neural Differentiation in Modeling
skos:prefLabel
Neural Differentiation in Modeling Neural Differentiation in Modeling
skos:notation
RIV/70883521:28140/09:63508011!RIV10-GA0-28140___
n3:aktivita
n4:P
n3:aktivity
P(GA102/09/1680)
n3:dodaniDat
n21:2010
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n3:druhVysledku
n16:D
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
329220
n3:idVysledku
RIV/70883521:28140/09:63508011
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Artificial Neural Network; modeling; approximation; feedforward network; artificial intelligence
n3:klicoveSlovo
n5:feedforward%20network n5:modeling n5:artificial%20intelligence n5:approximation n5:Artificial%20Neural%20Network
n3:kontrolniKodProRIV
[42A0E63E7B5F]
n3:mistoKonaniAkce
Brno
n3:mistoVydani
Brno
n3:nazevZdroje
MENDEL 2009 15th International Coference on Soft Computing
n3:obor
n10:BD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n17:GA102%2F09%2F1680
n3:rokUplatneniVysledku
n21:2009
n3:tvurceVysledku
Oplatková, Zuzana Zelinka, Ivan Tupý, Jaroslav
n3:typAkce
n19:WRD
n3:zahajeniAkce
2009-06-24+02:00
s:numberOfPages
354
n13:hasPublisher
Vysoké učení technické v Brně. Fakulta strojního inženýrství
n6:isbn
978-80-214-3675-6
n7:organizacniJednotka
28140