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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Separa??o cega de fontes lineares e n?o lineares usando algoritmo gen?tico, redes neurais artificiais RBF e negentropia de R?nyi como medida de independ?ncia

Damasceno, Nielsen Castelo 20 December 2010 (has links)
Made available in DSpace on 2014-12-17T14:55:50Z (GMT). No. of bitstreams: 1 NielsenCD_DISSERT.pdf: 3425927 bytes, checksum: 2a460ebc6b49fe832a4f35b40786bc47 (MD5) Previous issue date: 2010-12-20 / Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and R?nyi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations / Os m?todos convencionais para resolver o problema de separa??o cega de fontes n?o lineares em geral utilizam uma s?rie de restri??es ? obten??o da solu??o, levando muitas vezes a uma n?o perfeita separa??o das fontes originais e alto custo computacional. Neste trabalho, prop?e-se uma alternativa de medida de independ?ncia com base na teoria da informa??o e utilizam-se ferramentas da intelig?ncia artificial para resolver problemas de separa??o cega de fontes lineares e posteriormente n?o lineares. No modelo linear aplica-se algoritmos gen?ticos e a Negentropia de R?nyi como medida de independ?ncia para encontrar uma matriz de separa??o linear a partir de misturas lineares usando sinais de forma de ondas, ?udios e imagens. Faz-se uma compara??o com dois tipos de algoritmos de An?lise de Componentes Independentes bastante difundidos na literatura. Posteriormente, utiliza-se a mesma medida de independ?ncia como fun??o custo no algoritmo gen?tico para recuperar sinais de fontes que foram misturadas por fun??es n?o lineares a partir de uma rede neural artificial do tipo base radial. Algoritmos gen?ticos s?o poderosas ferramentas de pesquisa global e, portanto, bem adaptados para utiliza??o em problemas de separa??o cega de fontes. Os testes e as an?lises se d?o atrav?s de simula??es computacionais

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