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Statements

Subject Item
n2:RIV%2F70883521%3A28110%2F01%3A00000087%21RIV%2F2002%2FGA0%2F281102%2FN
rdf:type
skos:Concept n21:Vysledek
dcterms:description
In this contribution is shown, what are possible perspectives of some selected methods of artificial intelligence in astrophysics, especially in prediction. Two methods and one special approach were selected here. The first method is so called neural networks. They are discussed in the first part of this participation including some simulations for demonstration. They are followed by discussion of fractal geometry and its possibility in time series processing. In the second part is shown how can be usedevolutionary algorithms for retrieval of suitable predictive models. Two new different algorithms were used for simulations described here. The first one was differential evolution (DE) and the second one was Self-Organizing Migrating Algorithm (SOMA). Both algorithms were used in the same way to find the best model whose response is comparable with given time series as much as possible. Problem was build up like problem of optimization where the cost function was based on difference between original t In this contribution is shown, what are possible perspectives of some selected methods of artificial intelligence in astrophysics, especially in prediction. Two methods and one special approach were selected here. The first method is so called neural networks. They are discussed in the first part of this participation including some simulations for demonstration. They are followed by discussion of fractal geometry and its possibility in time series processing. In the second part is shown how can be usedevolutionary algorithms for retrieval of suitable predictive models. Two new different algorithms were used for simulations described here. The first one was differential evolution (DE) and the second one was Self-Organizing Migrating Algorithm (SOMA). Both algorithms were used in the same way to find the best model whose response is comparable with given time series as much as possible. Problem was build up like problem of optimization where the cost function was based on difference between original t
dcterms:title
ARTIFICIAL INTELLIGENCE IN ASTROPHYSICS ARTIFICIAL INTELLIGENCE IN ASTROPHYSICS
skos:prefLabel
ARTIFICIAL INTELLIGENCE IN ASTROPHYSICS ARTIFICIAL INTELLIGENCE IN ASTROPHYSICS
skos:notation
RIV/70883521:28110/01:00000087!RIV/2002/GA0/281102/N
n3:strany
v tisku
n3:aktivita
n4:Z n4:P
n3:aktivity
P(GA102/00/0526), P(GA102/99/1292), Z(MSM 265200014)
n3:dodaniDat
n16:2002
n3:domaciTvurceVysledku
n8:3433390
n3:druhVysledku
n13:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
673886
n3:idVysledku
RIV/70883521:28110/01:00000087
n3:jazykVysledku
n11:eng
n3:klicovaSlova
neural network, fractal geometry, prediction, solar activity, Elliott's wave, SOMA, DE, prediction, identification, evolution, migration, and self-organization
n3:klicoveSlovo
n6:identification n6:Elliott%27s%20wave n6:evolution n6:DE n6:and%20self-organization n6:solar%20activity n6:neural%20network n6:migration n6:SOMA n6:prediction n6:fractal%20geometry
n3:kontrolniKodProRIV
[7671772FB4D3]
n3:mistoKonaniAkce
Brno
n3:mistoVydani
Brno
n3:nazevZdroje
HaPMK
n3:obor
n19:BN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:pocetUcastnikuAkce
0
n3:pocetZahranicnichUcastnikuAkce
0
n3:projekt
n14:GA102%2F99%2F1292 n14:GA102%2F00%2F0526
n3:rokUplatneniVysledku
n16:2001
n3:tvurceVysledku
Zelinka, Ivan
n3:typAkce
n10:WRD
n3:zahajeniAkce
2001-11-08+01:00
n3:zamer
n12:MSM%20265200014
s:numberOfPages
20
n18:hasPublisher
Hvězdárna a planetárium Mikuláše Koperníka
n17:organizacniJednotka
28110