No / This paper presents a novel concept to describe the couplings among the outputs of the stochastic systems which are represented by NARMA models. Compared with the traditional coupling description, the presented concept can be considered as an extension using statistical independence theory. Based on this concept, the decoupling control in statistical sense is established with the necessary and sufficient conditions for complete decoupling. Since the complete decoupling is difficult to achieve, a control algorithm has been developed using the Cauchy-Schwarz mutual information criterion. Without modifying the existing control loop, this algorithm supplies a compensative controller to minimise the statistical couplings of the system outputs and the local stability has been analysed. In addition, a further discussion illustrates the combination of the presented control algorithm and data-based mutual information estimation. Finally, a numerical example is given to show the feasibility and efficiency of the proposed algorithm.

Made available in DSpace on 2014-12-17T14:55:09Z (GMT). No. of bitstreams: 1 DanielSAA_TESE_inicio_pag67.pdf: 3521346 bytes, checksum: 030bba7c8ca800b8151b345676b6759c (MD5) Previous issue date: 2013-03-18 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Currently, one of the biggest challenges for the field of data mining is to perform cluster analysis on complex data. Several techniques have been proposed but, in general, they can only achieve good results within specific areas providing no consensus of what would be the best way to group this kind of data. In general, these techniques fail due to non-realistic assumptions about the true probability distribution of the data. Based on this, this thesis proposes a new measure based on Cross Information Potential that uses representative points of the dataset and statistics extracted directly from data to measure the interaction between groups. The proposed approach allows us to use all advantages of this information-theoretic descriptor and solves the limitations imposed on it by its own nature. From this, two cost functions and three algorithms have been proposed to perform cluster analysis. As the use of Information Theory captures the relationship between different patterns, regardless of assumptions about the nature of this relationship, the proposed approach was able to achieve a better performance than the main algorithms in literature. These results apply to the context of synthetic data designed to test the algorithms in specific situations and to real data extracted from problems of different fields / Atualmente, um dos maiores desafios para o campo de minera??o de dados ? realizar a an?lise de agrupamentos em dados complexos. At? o momento, diversas t?cnicas foram propostas mas, em geral, elas s? conseguem atingir bons resultados dentro de dom?nios espec?ficos, n?o permitindo, dessa maneira, que exista um consenso de qual seria a melhor forma para agrupar dados. Essas t?cnicas costumam falhar por fazer suposi??es nem sempre realistas sobre a distribui??o de probabilidade que modela os dados. Com base nisso, o trabalho proposto neste documento cria uma nova medida baseada no Potencial de Informa??o Cruzado que utiliza pontos representativos do conjunto de dados e a estat?stica extra?da diretamente deles para medir a intera??o entre grupos. A abordagem proposta permite usar todas as vantagens desse descritor de informa??o e contorna as limita??es impostas a ele pela sua pr?pria forma de funcionamento. A partir disso, duas fun??es custo de otimiza??o e tr?s algoritmos foram constru?dos para realizar a an?lise de agrupamentos. Como o uso de Teoria da Informa??o permite capturar a rela??o entre diferentes padr?es, independentemente de suposi??es sobre a natureza dessa rela??o, a abordagem proposta foi capaz de obter um desempenho superior aos principais algoritmos citados na literatura. Esses resultados valem tanto para o contexto de dados sint?ticos desenvolvidos para testar os algoritmos em situa??es espec?ficas quanto em dados extra?dos de problemas reais de diferentes naturezas