Orientadores: João Marcos Travassos Romano, Romis Ribeiro de Faissol Attux / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-12T15:14:54Z (GMT). No. of bitstreams: 1 Dias_TiagoMacedo_M.pdf: 3219855 bytes, checksum: 5572e53d65cb457f420e78b3150dd6ee (MD5) Previous issue date: 2008 / Resumo: Esta dissertação se propõe a estudar um novo método para separação cega de fontes baseado no modelo Post-Nonlinear, que une uma ferramenta de busca global baseada em computação bioinspirada a uma etapa de busca local conduzida pelo algoritmo FastICA. A idéia subjacente à proposta é procurar obter soluções precisas e eficientes usando de maneira parcimoniosa os recursos computacionais disponíveis. A nova proposta foi testada em diferentes cenários, e, em todos os casos, estabeleceram-se comparações com uma abordagem alternativa, cujo passo de otimização não inclui o estágio de busca local (ou "memética"). Os resultados obtidos por meio de simulações indicam que um bom compromisso entre desempenho e custo computacional foi, de fato, atingido. / Resumo: Esta dissertação se propõe a estudar um novo método para separação cega de fontes baseado no modelo Post-Nonlinear, que une uma ferramenta de busca global baseada em computação bioinspirada a uma etapa de busca local conduzida pelo algoritmo FastICA. A idéia subjacente à proposta é procurar obter soluções precisas e eficientes usando de maneira parcimoniosa os recursos computacionais disponíveis. A nova proposta foi testada em diferentes cenários, e, em todos os casos, estabeleceram-se comparações com uma abordagem alternativa, cujo passo de otimização não inclui o estágio de busca local (ou "memética"). Os resultados obtidos por meio de simulações indicam que um bom compromisso entre desempenho e custo computacional foi, de fato, atingido. / Abstract: This work deals with a new method for source separation of Post-Nonlinear mixtures that brings together an evolutionary-based global search and a local search step based on the FastICA algorithm. The rationale of the proposal is to attempt to obtain efficient and precise solutions using with parsimony the available computational resources. The new proposal was tested in different scenarios and, in all cases, we attempted to establish grounds for comparison with an alternative approach whose optimization step does not include the local (memetic) search stage. Simulation results indicate that a good tradeoff between performance and computational cost was indeed reached. / Abstract: This work deals with a new method for source separation of Post-Nonlinear mixtures that brings together an evolutionary-based global search and a local search step based on the FastICA algorithm. The rationale of the proposal is to attempt to obtain efficient and precise solutions using with parsimony the available computational resources. The new proposal was tested in different scenarios and, in all cases, we attempted to establish grounds for comparison with an alternative approach whose optimization step does not include the local (memetic) search stage. Simulation results indicate that a good tradeoff between performance and computational cost was indeed reached. / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

Der Fokus dieser Dissertation liegt auf den Verbindungen zwischen ICA (Independent Component Analysis - Unabhängige Komponenten Analyse) und SFA (Slow Feature Analysis - Langsame Eigenschaften Analyse). Um einen Vergleich zwischen beiden Methoden zu ermöglichen wird CuBICA2, ein ICA Algorithmus basierend nur auf Statistik zweiter Ordnung, d.h. Kreuzkorrelationen, vorgestellt. Dieses Verfahren minimiert zeitverzögerte Korrelationen zwischen Signalkomponenten, um die statistische Abhängigkeit zwischen denselben zu reduzieren. Zusätzlich wird eine alternative SFA-Formulierung vorgestellt, die mit CuBICA2 verglichen werden kann. Im Falle linearer Gemische sind beide Methoden äquivalent falls nur eine einzige Zeitverzögerung berücksichtigt wird. Dieser Vergleich kann allerdings nicht auf mehrere Zeitverzögerungen erweitert werden. Für ICA lässt sich zwar eine einfache Erweiterung herleiten, aber ein ähnliche SFA-Erweiterung kann nicht im originären SFA-Sinne (SFA extrahiert die am langsamsten variierenden Signalkomponenten aus einem gegebenen Eingangssignal) interpretiert werden. Allerdings kann eine im SFA-Sinne sinnvolle Erweiterung hergeleitet werden, welche die enge Verbindung zwischen der Langsamkeit eines Signales (SFA) und der zeitlichen Vorhersehbarkeit desselben verdeutlich. Im Weiteren wird CuBICA2 und SFA kombiniert. Das Resultat kann aus zwei Perspektiven interpretiert werden. Vom ICA-Standpunkt aus führt die Kombination von CuBICA2 und SFA zu einem Algorithmus, der das Problem der nichtlinearen blinden Signalquellentrennung löst. Vom SFA-Standpunkt aus ist die Kombination eine Erweiterung der standard SFA. Die standard SFA extrahiert langsam variierende Signalkomponenten die untereinander unkorreliert sind, dass heißt statistisch unabhängig bis zur zweiten Ordnung. Die Integration von ICA führt nun zu Signalkomponenten die mehr oder weniger statistisch unabhängig sind. / Within this thesis, we focus on the relation between independent component analysis (ICA) and slow feature analysis (SFA). To allow a comparison between both methods we introduce CuBICA2, an ICA algorithm based on second-order statistics only, i.e.\ cross-correlations. In contrast to algorithms based on higher-order statistics not only instantaneous cross-correlations but also time-delayed cross correlations are considered for minimization. CuBICA2 requires signal components with auto-correlation like in SFA, and has the ability to separate source signal components that have a Gaussian distribution. Furthermore, we derive an alternative formulation of the SFA objective function and compare it with that of CuBICA2. In the case of a linear mixture the two methods are equivalent if a single time delay is taken into account. The comparison can not be extended to the case of several time delays. For ICA a straightforward extension can be derived, but a similar extension to SFA yields an objective function that can not be interpreted in the sense of SFA. However, a useful extension in the sense of SFA to more than one time delay can be derived. This extended SFA reveals the close connection between the slowness objective of SFA and temporal predictability. Furthermore, we combine CuBICA2 and SFA. The result can be interpreted from two perspectives. From the ICA point of view the combination leads to an algorithm that solves the nonlinear blind source separation problem. From the SFA point of view the combination of ICA and SFA is an extension to SFA in terms of statistical independence. Standard SFA extracts slowly varying signal components that are uncorrelated meaning they are statistically independent up to second-order. The integration of ICA leads to signal components that are more or less statistically independent.