Uncertainty Reduction Method Based on Statistics and Parallel Evolutionary Algorithms

In many scientific areas, the use of models to represent physical systems has become a common strategy. These models receive some input parameters representing some particular conditions and they provide an output representing the evolution of the system. Usually, these models are integrated into si...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Bianchini, Germán, Caymes-Scutari, Paola
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2011
Materias:
Ciencias Informáticas
Uncertainty Reduction Method
Parallel Evolutionary Algorithm
Statistics
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/126112
https://40jaiio.sadio.org.ar/sites/default/files/T2011/HPC/623.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-126112
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Uncertainty Reduction Method
Parallel Evolutionary Algorithm
Statistics
spellingShingle Ciencias Informáticas
Uncertainty Reduction Method
Parallel Evolutionary Algorithm
Statistics
Bianchini, Germán
Caymes-Scutari, Paola
Uncertainty Reduction Method Based on Statistics and Parallel Evolutionary Algorithms
topic_facet Ciencias Informáticas
Uncertainty Reduction Method
Parallel Evolutionary Algorithm
Statistics
description In many scientific areas, the use of models to represent physical systems has become a common strategy. These models receive some input parameters representing some particular conditions and they provide an output representing the evolution of the system. Usually, these models are integrated into simulation tools that can be executed on a computational system. A particular case where models are very useful is the prediction of Forest Fire propagation. Therefore, the use of models is very relevant to estimate fire risk and to predict fire behaviour. However, in many cases the models present a series of limitations. Such restrictions are due to the need for a large number of input parameters and, usually, such parameters present some uncertainty due to the impossibility of measuring all of them in real time. In consequence, they have to be estimated from indirect measurements. To overcome this drawback and improve the quality of the prediction, in this work we propose a method that combines Statistical Analysis and Parallel Evolutionary Algorithms.
format Objeto de conferencia
Objeto de conferencia
author Bianchini, Germán
Caymes-Scutari, Paola
author_facet Bianchini, Germán
Caymes-Scutari, Paola
author_sort Bianchini, Germán
title Uncertainty Reduction Method Based on Statistics and Parallel Evolutionary Algorithms
title_short Uncertainty Reduction Method Based on Statistics and Parallel Evolutionary Algorithms
title_full Uncertainty Reduction Method Based on Statistics and Parallel Evolutionary Algorithms
title_fullStr Uncertainty Reduction Method Based on Statistics and Parallel Evolutionary Algorithms
title_full_unstemmed Uncertainty Reduction Method Based on Statistics and Parallel Evolutionary Algorithms
title_sort uncertainty reduction method based on statistics and parallel evolutionary algorithms
publishDate 2011
url http://sedici.unlp.edu.ar/handle/10915/126112
https://40jaiio.sadio.org.ar/sites/default/files/T2011/HPC/623.pdf
work_keys_str_mv AT bianchinigerman uncertaintyreductionmethodbasedonstatisticsandparallelevolutionaryalgorithms
AT caymesscutaripaola uncertaintyreductionmethodbasedonstatisticsandparallelevolutionaryalgorithms
bdutipo_str Repositorios
_version_ 1764820450112700417

Ejemplares similares