Correntropia complexa: definição, propriedades e aplicações
Recent studies have demonstrated that correntropy is an efficient tool for analyzing higher-order statistical moments in non-Gaussian noise environments. Although correntropy has been used with complex-valued data, no theoretical study was pursued to elucidate its properties, nor how to best use it...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/28615 |
| Acceso en línea: | https://repositorio.ufrn.br/jspui/handle/123456789/28615 |
| Access Level: | acceso abierto |
| Palabra clave: | Correntropia Dados complexos Medida de similaridade CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
| Sumario: | Recent studies have demonstrated that correntropy is an efficient tool for analyzing higher-order statistical moments in non-Gaussian noise environments. Although correntropy has been used with complex-valued data, no theoretical study was pursued to elucidate its properties, nor how to best use it for optimization. By using a probabilistic interpretation, this work presents a novel similarity measure between two complex-valued random variables, which is defined as complex correntropy. Its properties are studied as well as a new recursive solution for the Maximum Complex Correntropy Criterion (MCCC) and two algorithms are derived, one based on the ascendent gradient and a second one on a fixed-point solution. Simulations were made in order to evaluate how robust this new measure is to impulsive noise in different problems: liner system identification, channel equalization and in a compressive sensing problem. It is also shown the application of complex correntropy as a tool to analyse the similarity between angles. The results demonstrate prominent advantages of the proposed method when compared with the classical algorithms in the literature. |
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