Genetický algoritmus pro optimalizaci vah neuronové sítě.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F02%3APU30577" target="_blank" >RIV/00216305:26210/02:PU30577 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

The genetic algorithm used for the optimization of weights and biases of neural networks

Popis výsledku v původním jazyce

The life of the insulating systems of electric rotary machines is strongly dependent upon electrical and thermal features of the insulating material used. The subject of the diagnostics is to specify the condition of insulation used. At present days, themost popular diagnostic tools are the methods of artificial intelligence, and one method is a neural network. However, this tool has many variable parameters, and the resulting effect is dependent upon its suitable setting. Weights and biases that conneect neurones in each layer of the neural network are ranked among these parameters. This paper is concentrated on the use of genetic algorithms for the optimization of weights and biases of the neural network that is used as a diagnostic tool for windinginsulation of electric rotary machines. In this case, the optimization of weights and biases means that the neural network exhibits the minimum absolute mean errors for the diagnostics of winding insulation. The paper describes the archite

Název v anglickém jazyce

The genetic algorithm used for the optimization of weights and biases of neural networks

Popis výsledku anglicky

The life of the insulating systems of electric rotary machines is strongly dependent upon electrical and thermal features of the insulating material used. The subject of the diagnostics is to specify the condition of insulation used. At present days, themost popular diagnostic tools are the methods of artificial intelligence, and one method is a neural network. However, this tool has many variable parameters, and the resulting effect is dependent upon its suitable setting. Weights and biases that conneect neurones in each layer of the neural network are ranked among these parameters. This paper is concentrated on the use of genetic algorithms for the optimization of weights and biases of the neural network that is used as a diagnostic tool for windinginsulation of electric rotary machines. In this case, the optimization of weights and biases means that the neural network exhibits the minimum absolute mean errors for the diagnostics of winding insulation. The paper describes the archite

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

—

Projekt

<a href="/cs/project/GA102%2F03%2F0621" target="_blank" >GA102/03/0621: Ireverzibilní procesy v elektroizolačních materiálech pro vysoké teploty</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2002

Kód důvěrnosti údajů

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

Název statě ve sborníku

Study and control of corrosion in the perspective of sustainable development of urban distribution grids

ISBN

973-95041-3-2

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

72-76

Název nakladatele

S.C.ICPE

Místo vydání

Constanta, Rumunsko

Místo konání akce

Constanta

Datum konání akce

6. 6. 2002

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—