Evolutionary design and training of artificial neural networks

Result code in 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>
Alternative codes found
RIV/61989100:27740/18:10241929
Result on the web
<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>

Result language
angličtina
Original language name
Evolutionary design and training of artificial neural networks
Original language description
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.
Czech name
—
Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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