Artificial Neural Network Utilization for FSO Link Performance Estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00217539" target="_blank" >RIV/68407700:21230/14:00217539 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Artificial Neural Network Utilization for FSO Link Performance Estimation

Original language description

This paper describes FSO link performance prediction based on available meteorological data using different Artificial Neural Network (ANN) approaches. Several types of ANNs were compared and their performance were evaluated. The paper introduces an ANNapplication utilizing real delayed data. This approach has been validated to be more precise than common feed-forward neural networks.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JA - Electronics and optoelectronics

OECD FORD branch

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Project

<a href="/en/project/LD12058" target="_blank" >LD12058: Research of Ambient Influences on Novel Broadband Optical Wireless Systems (RAINBOWS)</a><br>

Continuities

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

Publication year

2014

Confidentiality

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

Name of the periodical

Radioengineering

ISSN

1210-2512

e-ISSN

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Volume of the periodical

23

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

6

Pages from-to

474-479

UT code for WoS article

000334729600024

EID of the result in the Scopus database

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