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rdf:type
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Description
| - The layered neural networks are still considered as very general tools for approximation and they became popular especially for their simple implementation. The practical usage is, however, nontrivial in the choice of an appropriate architecture. The presented contribution is concerned with the development of a simple neural network with the selfadaptive architecture, which can enable easier neural network applications in engineering problems. Its approximation abilities are tested on several mathematical problems and two different modes of material parameters' identification problem. In the first one, the neural network is used to approximate the numerical model predicting the response for a given set of material parameters and loading. The second mode employs the neural network for constructing an inverse model, where material parameters are directly predicted for a given (measured) response.
- The layered neural networks are still considered as very general tools for approximation and they became popular especially for their simple implementation. The practical usage is, however, nontrivial in the choice of an appropriate architecture. The presented contribution is concerned with the development of a simple neural network with the selfadaptive architecture, which can enable easier neural network applications in engineering problems. Its approximation abilities are tested on several mathematical problems and two different modes of material parameters' identification problem. In the first one, the neural network is used to approximate the numerical model predicting the response for a given set of material parameters and loading. The second mode employs the neural network for constructing an inverse model, where material parameters are directly predicted for a given (measured) response. (en)
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Title
| - Artificial Neural Network as Universal Approximation of Nonlinear Relations
- Artificial Neural Network as Universal Approximation of Nonlinear Relations (en)
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skos:prefLabel
| - Artificial Neural Network as Universal Approximation of Nonlinear Relations
- Artificial Neural Network as Universal Approximation of Nonlinear Relations (en)
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skos:notation
| - RIV/68407700:21110/10:00169598!RIV11-MSM-21110___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
| - P(FT-TA4/100), Z(MSM6840770003)
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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/68407700:21110/10:00169598
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - artificial neural network; multi-layer perceptron; approximation; nonlinear relations; back-propagation; parameter identification (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
| - Proceedings of International Conference on Modelling and Simulation 2010 in Prague
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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
| - Kučerová, Anna
- Mareš, Tomáš
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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http://linked.open...n/vavai/riv/zamer
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number of pages
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http://purl.org/ne...btex#hasPublisher
| - České vysoké učení technické v Praze
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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is http://linked.open...avai/riv/vysledek
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