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Master's Dissertation
DOI
10.11606/D.18.2014.tde-17122014-112052
Document
Author
Full name
Guilherme Serpa Sestito
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2014
Supervisor
Committee
Brandão, Dennis (President)
Ogashawara, Osmar
Spatti, Danilo Hernane
Title in Portuguese
Uma proposta metodológica para a previsão do Throughput durante a inicialização de redes Profinet através de redes neurais artificiais
Keywords in Portuguese
Throughput
Indicadores de desempenho
Netload
Profinet
RNA
Abstract in Portuguese
Este trabalho propõe o desenvolvimento de uma metodologia para o cálculo do volume de tráfego durante o período de inicialização de uma rede Profinet. O tráfego de dados é um dos indicadores de desempenho criados para garantir a qualidade dos protocolos baseados em Real Time Ethernet (RTE). Neste contexto, buscou-se na literatura uma forma de classificar o tráfego de acordo com a sua magnitude e mensurar seu efeito na comunicação. Dados provenientes de redes criadas em laboratório foram coletados e aplicados a uma Rede Neural Artificial visando generalizar o conhecimento adquirido. O uso dado a RNA foi de estimação da função de interesse. Os resultados obtidos após o processamento dos dados reais são considerados satisfatórios e condizentes às expectativas dessa dissertação, já que se buscou, por razões inerentes ao problema estudado, um erro relativo inferior 3%. Conclui-se que a metodologia apresentada é factível e aplicável ao meio industrial, podendo ser parte de uma ferramenta mais completa, como os analisadores de redes Profinet.
Title in English
A proposal of a methodology to preview Throughput in Profinet network using Artificial Neural Networks
Keywords in English
Throughput
ANN
Netload
Performance indicators
Profinet
Abstract in English
This paper suggests the development of a methodology to calculate the traffic volume during the starting period of a Profinet network. The data traffic is one of the development indicators created to guarantee the protocols quality based on Real Time Ethernet (RTE). In this context, a way of classifying the traffic according to its magnitude and of measuring its effect in the communication was searched in the literature. Data deriving from networks created in laboratory were collected and applied into an Artificial Neural Network aiming to generalize the acquired knowledge. The ANN was used to estimate the function of interest. The results obtained after the real data processing are considered satisfactory and suitable to the expectations of this dissertation where the relative error inferior to 3%, for reasons intrinsic to the studied problem, was searched. It is concluded that the methodology presented is feasible and applicable in the industrial field, where it can be part of a more complete tool, as the Profinet network analyzers.
 
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Guilherme.pdf (3.57 Mbytes)
Publishing Date
2015-01-20
 
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