Evolutionary design and training of artificial neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241929" target="_blank" >RIV/61989100:27240/18:10241929 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/18:10241929

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-91253-0_40" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-91253-0_40</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-91253-0_40" target="_blank" >10.1007/978-3-319-91253-0_40</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary design and training of artificial neural networks

Popis výsledku v původním jazyce

The dynamics of neural networks and evolutionary algorithms share common attributes and based on many research papers it seems to be that from dynamic point of view are both systems indistinguishable. In order to compare them mutually from this point of view, artificial neural networks, as similar as possible to natural one, are needed. In this paper is described part of our research that is focused on the synthesis of artificial neural networks. Since most current ANN structures are not common in nature, we introduce a method of a complex network synthesis using network growth model, considered as a neural network. Synaptic weights of the synthesized ANN are then trained by an evolutionary algorithm to respond to an input training set successfully. (C) Springer International Publishing AG, part of Springer Nature 2018.

Název v anglickém jazyce

Evolutionary design and training of artificial neural networks

Popis výsledku anglicky

The dynamics of neural networks and evolutionary algorithms share common attributes and based on many research papers it seems to be that from dynamic point of view are both systems indistinguishable. In order to compare them mutually from this point of view, artificial neural networks, as similar as possible to natural one, are needed. In this paper is described part of our research that is focused on the synthesis of artificial neural networks. Since most current ANN structures are not common in nature, we introduce a method of a complex network synthesis using network growth model, considered as a neural network. Synaptic weights of the synthesized ANN are then trained by an evolutionary algorithm to respond to an input training set successfully. (C) Springer International Publishing AG, part of Springer Nature 2018.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 10841

ISBN

978-3-319-91252-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

427-437

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Zakopane

Datum konání akce

3. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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