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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 (en)
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Title
| - Neural Differentiation in Modeling
- Neural Differentiation in Modeling (en)
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skos:prefLabel
| - Neural Differentiation in Modeling
- Neural Differentiation in Modeling (en)
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skos:notation
| - RIV/70883521:28140/09:63508011!RIV10-GA0-28140___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/70883521:28140/09:63508011
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Artificial Neural Network; modeling; approximation; feedforward network; artificial intelligence (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - MENDEL 2009 15th International Coference on Soft Computing
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Oplatková, Zuzana
- Zelinka, Ivan
- Tupý, Jaroslav
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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number of pages
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http://purl.org/ne...btex#hasPublisher
| - Vysoké učení technické v Brně. Fakulta strojního inženýrství
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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