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Statements

Subject Item
n2:RIV%2F70883521%3A28140%2F12%3A43868836%21RIV13-MSM-28140___
rdf:type
skos:Concept n11:Vysledek
dcterms:description
: This report describes a feed forward Artificial Neural Network (ANN) synthesis via an Analytic Programming (AP) by means of the ANN creation, learning and optimization. This process encompasses four different fields: Evolutionary Algorithms, Symbolic Regression, ANN and parallel computing to successfully synthesize a suitable ANN within a reasonable time. ANN synthesis proved to be a useful and efficient tool for nonlinear modeling and its results were applied to intelligent system controlling an energetic framework of an urban agglomeration. Furthermore, the ANN synthesis proved to have the ability to synthesize smaller ANN than the Genetic Programming (GP) while simultaneously almost infinitely complex ANN can be synthesized by the application of multiple evolution loops. This process can also produce ANN with feed forward branching, which is an unavailable quality for the GP. : This report describes a feed forward Artificial Neural Network (ANN) synthesis via an Analytic Programming (AP) by means of the ANN creation, learning and optimization. This process encompasses four different fields: Evolutionary Algorithms, Symbolic Regression, ANN and parallel computing to successfully synthesize a suitable ANN within a reasonable time. ANN synthesis proved to be a useful and efficient tool for nonlinear modeling and its results were applied to intelligent system controlling an energetic framework of an urban agglomeration. Furthermore, the ANN synthesis proved to have the ability to synthesize smaller ANN than the Genetic Programming (GP) while simultaneously almost infinitely complex ANN can be synthesized by the application of multiple evolution loops. This process can also produce ANN with feed forward branching, which is an unavailable quality for the GP.
dcterms:title
NEURAL NETWORK SYNTHESIS NEURAL NETWORK SYNTHESIS
skos:prefLabel
NEURAL NETWORK SYNTHESIS NEURAL NETWORK SYNTHESIS
skos:notation
RIV/70883521:28140/12:43868836!RIV13-MSM-28140___
n11:predkladatel
n12:orjk%3A28140
n3:aktivita
n13:S n13:P
n3:aktivity
P(ED2.1.00/03.0089), S
n3:dodaniDat
n10:2013
n3:domaciTvurceVysledku
n20:5161940
n3:druhVysledku
n9:D
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
153862
n3:idVysledku
RIV/70883521:28140/12:43868836
n3:jazykVysledku
n5:eng
n3:klicovaSlova
Neural Network, Analytic Programming, SOMA, optimization
n3:klicoveSlovo
n16:Analytic%20Programming n16:Neural%20Network n16:SOMA n16:optimization
n3:kontrolniKodProRIV
[AE768DE1D2F7]
n3:mistoKonaniAkce
Brno
n3:mistoVydani
Brno
n3:nazevZdroje
18th International Conference on Soft Computing
n3:obor
n14:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n15:ED2.1.00%2F03.0089
n3:rokUplatneniVysledku
n10:2012
n3:tvurceVysledku
Vařacha, Pavel
n3:typAkce
n21:WRD
n3:zahajeniAkce
2012-06-27+02:00
s:issn
1803-3814
s:numberOfPages
6
n4:hasPublisher
Vysoké učení technické v Brně
n17:isbn
978-80-214-4540-6
n6:organizacniJednotka
28140