Adaptive empirical distributions in the framework of inverse problems
| Autor: | Maria Amélia Ramos Loja, J. I. Barbosa, Alda Carvalho, Tiago Silva, Nuno M. M. Maia |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Inverse sampling Population Computational Mechanics 02 engineering and technology 01 natural sciences Set (abstract data type) 0203 mechanical engineering Empirical CDF 0101 mathematics education Mathematics education.field_of_study Cumulative distribution function Two samples Kolmogorov-Smirnov goodness-of-fit test Extension (predicate logic) Inverse problem Empirical distribution function 010101 applied mathematics Computational Mathematics 020303 mechanical engineering & transports Differential evolution Adaptive empirical distributions Minification |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 1550-2295 1550-2287 |
| Popis: | 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. info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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