Publicação
Adaptive empirical distributions in the framework of inverse problems
| Resumo: | This article presents an innovative framework regarding an inverse problem. One presents the extension of a global optimization algorithm to estimate not only an optimal set of modeling parameters, but also their optimal distributions. Regarding its characteristics, differential evolution algorithm is used to demonstrate this extension, although other population-based algorithms may be considered. The adaptive empirical distributions algorithm is here introduced for the same purpose. Both schemes rely on the minimization of the dissimilarity between the empirical cumulative distribution functions of two data sets, using a goodness-of-fit test to evaluate their resemblance. |
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| Autores principais: | Silva, Tiago |
| Outros Autores: | Loja, Amélia; Carvalho, Alda; Maia, Nuno. M.; Barbosa, Joaquim |
| Assunto: | Adaptive empirical distributions Differential evolution Empirical CDF Inverse problem Inverse sampling Two samples Kolmogorov-Smirnov goodness-of-fit test |
| Ano: | 2017 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Lisboa |
| Resumo: | This article presents an innovative framework regarding an inverse problem. One presents the extension of a global optimization algorithm to estimate not only an optimal set of modeling parameters, but also their optimal distributions. Regarding its characteristics, differential evolution algorithm is used to demonstrate this extension, although other population-based algorithms may be considered. The adaptive empirical distributions algorithm is here introduced for the same purpose. Both schemes rely on the minimization of the dissimilarity between the empirical cumulative distribution functions of two data sets, using a goodness-of-fit test to evaluate their resemblance. |
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