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Description
  • Design of optimal input signal for system modeled by multi-layer perceptron network is treated. Because the true system is unknown, the design can be constructed only from the actually obtained model. However, neural networks with the same structure differing only in parameters values are able to approximate various nonlinear mappings therefore it is crucial maximally to use available informations to select suitable input data. Hence a global estimation method allowing to determine conditional probability density functions of network parameters will be used. The Gaussian sum approach based on approximation of arbitrary probability density function by a sum of normal distributions seems to be suitable to use. This approach is a less computationally demanding alternative to the sequential Monte Carlo methods and gives better results than the commonly used prediction error methods. The properties of the proposed experimental design are demonstrated in a numerical example.
  • Design of optimal input signal for system modeled by multi-layer perceptron network is treated. Because the true system is unknown, the design can be constructed only from the actually obtained model. However, neural networks with the same structure differing only in parameters values are able to approximate various nonlinear mappings therefore it is crucial maximally to use available informations to select suitable input data. Hence a global estimation method allowing to determine conditional probability density functions of network parameters will be used. The Gaussian sum approach based on approximation of arbitrary probability density function by a sum of normal distributions seems to be suitable to use. This approach is a less computationally demanding alternative to the sequential Monte Carlo methods and gives better results than the commonly used prediction error methods. The properties of the proposed experimental design are demonstrated in a numerical example. (en)
  • Článek se zabývá návrhem optimálního vstupního signálu v úloze identifikace nelineárního stochastického systému neuronovou sítí. Vstupní signál je navržen na základě aktuálně získaného modelu. Klíčovou úlohu proto hraje vhodná metoda odhadu parametrů. Navržená metoda generování vstupního signálu využívá podmíněných hustot pravděpodobnosti parametrů místo běžně užívaných bodových odhadů. Z tohoto důvodu byla k odhadu parametrů sítě použita globální metoda Gaussových směsí. Navržená metoda umožňuje lépe poznat identifikovaný systém, než pokud by byly použity stávající metody návrhu vstupního signálu. (cs)
Title
  • Gaussian Sum Approach with Optimal Experiment Design for Neural Network
  • Gaussian Sum Approach with Optimal Experiment Design for Neural Network (en)
  • Metoda Gaussových směsí při návrhu optimálního vstupního signálu neuronové sítě (cs)
skos:prefLabel
  • Gaussian Sum Approach with Optimal Experiment Design for Neural Network
  • Gaussian Sum Approach with Optimal Experiment Design for Neural Network (en)
  • Metoda Gaussových směsí při návrhu optimálního vstupního signálu neuronové sítě (cs)
skos:notation
  • RIV/49777513:23520/07:00000046!RIV08-GA0-23520___
http://linked.open.../vavai/riv/strany
  • 425-430
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/05/2075), P(GP102/06/P202)
http://linked.open...vai/riv/dodaniDat
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  • 422982
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  • RIV/49777513:23520/07:00000046
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http://linked.open.../riv/klicovaSlova
  • System identification; optimal experiment design; nonlinear parameters estimation; probability density function; multi-layer perceptron network (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [4211FBBFE60D]
http://linked.open...v/mistoKonaniAkce
  • Honolulu
http://linked.open...i/riv/mistoVydani
  • Honolulu
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
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
  • Hering, Pavel
  • Šimandl, Miroslav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • ACTA Press
https://schema.org/isbn
  • 978-0-88986-676-8
http://localhost/t...ganizacniJednotka
  • 23520
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