The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on November 13, 2009). Thesis advisor: Dr. Guilherme DeSouza. Includes bibliographical references.

Thesis (M.S.)--University of Akron, Dept. of Computer Science, 2009. / "May, 2009." Title from electronic thesis title page (viewed 11/18/2009) Advisor, Kathy J. Liszka; Faculty Readers, Timothy W. O'Neil, Tim Margush; Department Chair, Wolfgang Pelz; Dean of the College, Chand Midha; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

Thesis (M.Sc. (Statistics)) - Rhodes University, 2009. / A thesis submitted to Rhodes University in partial fulfillment of the requirements for the degree of Master of Science in Mathematical Statistics.

Orientador: Antonio Fernando Crepaldi / Banca: Rogerio Andrade Flauzino / Banca: Kelton Augusto Pontara da Costa / Resumo: O aprendizado de máquina vem sendo utilizado em diferentes segmentos da área financeira, como na previsão de preços de ações, mercado de câmbio, índices de mercado e composição de carteira de investimento. Este trabalho busca comparar e combinar três tipos de algoritmos de aprendizagem de máquina, mais especificamente, o método Ensemble de Redes Neurais Artificias com as redes Multilayer Perceptrons (MLP), auto-regressiva com entradas exógenas (NARX) e Long Short-Term Memory (LSTM) para predição do Índice Bovespa. A amostra da série do Ibovespa foi obtida pelo Yahoo!Finance no período de 04 de janeiro de 2010 a 28 de dezembro de 2017, de periodicidade diária. Foram utilizadas as séries temporais referentes a cotação do Dólar, além de indicadores numéricos da Análise Técnica como variáveis independentes para compor a predição. Os algoritmos foram desenvolvidos através da linguagem Python usando framework Keras. Para avaliação dos algoritmos foram utilizadas as métricas de desempenho MSE, RMSE e MAPE, além da comparação entre as previsões obtidas e os valores reais. Os resultados das métricas indicam bom desempenho de predição pelo modelo Ensemble proposto, obtendo 70% de acerto no movimento do índice, porém, não conseguiu atingir melhores resultados que as redes MLP e NARX, ambas com 80% de acerto. / Abstract: Different segments of the financial area, such as the forecast of stock prices, the foreign exchange market, the market indices and the composition of investment portfolio, use machine learning. This work aims to compare and combine two types of machine learning algorithms, the Artificial Neural Network Ensemble method with Multilayer Perceptrons (MLP), auto-regressive with exogenous inputs (NARX) and Long Short-Term Memory (LSTM) for prediction of the Bovespa Index. The Bovespa time series samples were obtained daily, using Yahoo! Finance, from January 4th, 2010 to December 28th, 2017. Dollar quotation, Google trends and numerical indicators of the Technical Analysis were used as independent variables to compose the prediction. The algorithms were developed using Python and Keras framework. Finally, in order to evaluate the algorithms, the MSE, RMSE and MAPE performance metrics, as well as the comparison between the obtained predictions and the actual values, were used. The results of the metrics indicate good prediction performance by the proposed Ensemble model, obtaining a 70% accuracy in the index movement, but failed to achieve better results than the MLP and NARX networks, both with 80% accuracy. / Mestre

The residential property market accounts for a substantial proportion of UKeconomic activity. However, there is no reliable forecasting service to predict theperiodic housing market crises or to produce estimates of long-term sustainablevalue. This research examined the use of artificial neural networks, trained usingnational economic, social and residential property transaction time-series data, toforecast trends within the housing market. Artificial neural networks have previously been applied successfully to produceestimates of the open market value of a property over a limited time period withinsub-markets. They have also been applied to the prediction of time-series data in anumber of fields, including finance. This research sought to extend their applicationto time-series of house prices in order to forecast changes in the residential propertymarket at national and regional levels. Neural networks were demonstrated to be successful in producing time-seriesforecasts of changes in the housing market, particularly when combined in simplecommittees of networks. They successfully modelled the direction, timing and scaleof annual changes in house prices, both for an extremely volatile and difficult period(1987 to 1991) and for the period 1999 to 2001. Poor initial forecasting results forthe period 2002 onwards were linked to new conditions in the credit and housingmarkets, including changes in the loan to income ratio. Self-organising maps wereused to identify the onset of new market conditions. Neural networks trained with asubset of post-1998 data added to the training set improved their forecastingperformance, suggesting that they were able to incorporate the new conditions intothe models. Sensitivity analysis was used to identify and rank the network input variables underdifferent market conditions. The measure of changes in the house price index itselfwas found to have the greatest effect on future changes in prices. Predictionsurfaces were used to investigate the relationship between pairs of input variables. The results show that artificial neural networks, trained using national economic,social and residential property transaction time-series data, can be used to forecaststrends within the housing market under various market conditions.

Made available in DSpace on 2014-06-11T19:32:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-03-22Bitstream added on 2014-06-13T20:27:52Z : No. of bitstreams: 1 affonso_co_dr_guara.pdf: 1152405 bytes, checksum: 8f0081a68fda3c66a6f0ef4bcd0e9cac (MD5) / Para se obter produtos e serviços que atendam ao nível de produtividade exigida pelo mercado, deve-se otimizar vários fatores determinantes na usinagem da madeira. O atual objetivo da pesquisa em Inteligência Artificial dedica-se ao desenvolvimento de sistemas inteligentes flexíveis e auto ajustáveis, com vistas à diminuição da presença de operadores humanos, de forma que o controle destes processos seja realizado através de sistemas computacionais. A usinagem da madeira se caracteriza pela ação de vários agentes, que de forma geral, são muito complexos para serem representados de forma analítica, adicionalmente as respostas destes sistemas são não-lineares. Portanto, estas dificuldades na modelagem do processamento da madeira justificam a utilização de redes neurais como ferramenta para melhoria de processo, e consequente agregação de valor ao produto final. O objetivo deste trabalho foi utilizar a capacidade de aprendizagem e a generalização das redes neurais e outras técnicas de inteligência computacional no processamento de madeira. A metodologia utilizada consistiu em utilizar redes neurais do tipo Multilayer Perceptrons (MLP) associadas à Lógica Fuzzy para construção de controlador do processo de usinagem da madeira. Adicionalmente as redes neurais realizaram uma classificação de imagens com relação aos defeitos superficiais da madeira. Foi utilizadas bases de dados obtidas através dos processos reais de usinagem da madeira. Os resultados obtidos foram satisfatórios, o que confirma que as redes neurais foram uma... / In order to obtain products and services to exceed the level of productivity required by the market, many machining wood factors should be optimized. The current goal of research in Artificial Intelligence is dedicated to develop intelligent flexible systems, self-adjusting, to decrease the presence of human operators. The control of these processes is done through the help of computer systems composed from software and hardware. The modern industrial processes are characterized by the action of various agents that are generally too complex to be represented analytically, additionally answers these systems are non-linear. Therefore, these difficulties in modeling wood machining process justify the use of Neural Network as a tool for process improvement and to add value to the final product. Computational Intelligence techniques such as Neuro-Fuzzy Networks have been proved applicable to this problem, since they combine the ability to learn from examples and to generalize the information learned from the neural network with the ability of Fuzzy Logic to turn variables into linguistic rules. The objective of this work is to use the learning ability and generalization of neural networks and other techniques of Artificial Intelligence in machining materials, which have solid non-linear character. The results were satisfactory, thus confirming the neural... (Complete abstract click electronic access below)

Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009 / In real world systems such as the waste water treatment plants, the nonlinearities, uncertainty and complexity playa major role in their daily operations. Effective control of such systems variables requires robust control methods to accommodate the uncertainties and harsh environments. It has been shown that intelligent control systems have the ability to accommodate the system uncertain parameters. Techniques such as fuzzy logic, neural networks and genetic algorithms have had many successes in the field of control because they contain essential characteristics needed for the design of identifiers and controllers for complex systems where nonlinearities, complexity and uncertainties exist. Approaches based on neural networks have proven to be powerful tools for solvinq nonlinear control and optimisation problems. This is because neural networks have the ability to learn and approximate nonlinear functions arbitrarily wei!. The approximation capabilities of such networks can be used for the design of both identifiers and controllers. Basically, an artificial neural network is a computing architecture that consists of massively parallel interconnections of simple computing elements that provide insights into the kind of highly parallel computation that is carried out by biological nervous system. A large number of networks have been proposed and investigated with various topological structures. functionality and training algorithms for the purposes of identification and control of practical systems. For the purpose of this research thesis an approach for the investigation of the use of neural networks in identification, modelling and control of non-linear systems has been carried out. In particular, neural network identifiers and controllers have been designed for the control of the dissolved oxygen (DO) concentration of the activated sludge process in waste water treatment plants. These plants, being complex processes With several variables (states) and also affected by disturbances require some form of control in order to maintain the standards of effluent. DO concentration control in the aeration tank is the most widely used controlled variable. Nonlinearity is a feature that describes the dynamics of the dissolved oxygen process and therefore the DO estimation and control may not be sufficiently achieved with a conventional linear controller. Neural networks structures are proposed, trained and utilized for purposes of identification. modelling and design of NN controllers for nonlinear DO control. Algorithms and programs are developed using Matlab environment and are deployed on a hardware PLC platform. The research is limited to the feedforward multilayer perceptron and the recurrent neural networks for the identification and control. Control models considered are the direct inverse mode! control, internal mode! contra! and feedback linearizing control. Real-time implementation is limited to the lab-scale wastewater treatment plant.

Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009. Includes bibliographical references (leaves 95-100). / The use of Neural Networks in chemical engineering is well documented. There has also been an increase in research concerned with the explanatory capacity of Neural Networks although this has been hindered by the regard of Artificial Neural Networks (ANN’s) as a black box technology. Determining variable importance in complex systems that have many variables as found in the fields of ecology, water treatment, petrochemical production, and metallurgy, would reduce the variables to be used in optimisation exercises, easing complexity of the model and ultimately saving money. In the process engineering field, the use of data to optimise processes is limited if some degree of process understanding is not present. The project objective is to develop a methodology that uses Artificial Neural Network (ANN) technology and Multiple Linear Regression (MLR) to identify explanatory variables in a dataset and their importance on process outputs. The methodology is tested by using data that exhibits defined and well known numeric relationships. The numeric relationships are presented using four equations. The research project assesses the relative importance of the independent variables by using the “dropping method” on a regression model and ANN’s. Regression used traditionally to determine variable contribution could be unsuccessful if a highly nonlinear relationship exists. ANN’s could be the answer for this shortcoming. For differentiation, the explanatory variables that do not contribute significantly towards the output will be named “suspect variables”. Ultimately the suspect variables identified in the regression model and ANN should be the same, assuming a good regression model and network. The dummy variables introduced to the four equations are successfully identified as suspect variables. Furthermore, the degree of variable importance was determined using linear regression and ANN models. As the equations complexity increased, the linear regression models accuracy decreased, thus suspect variables are not correctly identified. The complexity of the equations does not affect the accuracy of the ANN model, and the suspect variables are correctly identified. The use of R2 and average error in establishing a criterion for identifying suspect variables is explored. It is established that the cumulative variable importance percentage (additive percentage), has to be below 5% for the explanatory variable to be considered a suspect variable. Combining linear regression and ANN provides insight into the importance of explanatory variables and indeed suspect variables and their contribution can be determined. Suspect variables can be eliminated from the model once identified simplifying the model, and increasing accuracy of the model.

Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009. / Neuro-fuzzy computing techniques have been approached and evaluated in areas of process control; researchers have recently begun to evaluate its potential in pattern recognition. Multi-component ion exchange is a non-linear process, which is difficult to model and simulate as there are many factors influencing the chemical process which are not well understood. In the past, empirical isotherm equations were used but there were definite shortcomings resulting in unreliable simulations. In this work, the use of artificial intelligence has therefore been researched to test the effectiveness in simulating ion exchange processes. The branch of artificial intelligence used was the adaptive neuro fuzzy inference system. The objective of this research was to develop a neuro-fuzzy software package to simulate ion exchange processes. The first step towards building this system was to collect data from laboratory scale ion exchange experiments. Different combinations of inputs (e.g. solution concentration, resin loading, impeller speed), were tested to determine whether it was necessary to monitor all available parameters. The software was developed in MSEXCEL where tools like SOLVER could be utilised whilst the code was written in Visual Basic. In order to compare the neuro-fuzzy simulations to previously used empirical methods, the Fritz and Schluender isotherm was used to model and simulate the same data. The results have shown that both methods were adequate but the neuro-fuzzyapproach was the more appropriate method. After completion of this study, it could be concluded that a neuro-fuzzy system does not always have the ability to describe ion exchange processes adequately.

Orientador: Marcos Tadeu Tiburcio Gonçalves / Banca: Manoel Cleber de Sampaio Alves / Banca: Maria Angelica Martins Costa / Banca: Raquel Gonçalves / Banca: Ivaldo de Domenico Valarelli / Resumo: Para se obter produtos e serviços que atendam ao nível de produtividade exigida pelo mercado, deve-se otimizar vários fatores determinantes na usinagem da madeira. O atual objetivo da pesquisa em Inteligência Artificial dedica-se ao desenvolvimento de sistemas inteligentes flexíveis e auto ajustáveis, com vistas à diminuição da presença de operadores humanos, de forma que o controle destes processos seja realizado através de sistemas computacionais. A usinagem da madeira se caracteriza pela ação de vários agentes, que de forma geral, são muito complexos para serem representados de forma analítica, adicionalmente as respostas destes sistemas são não-lineares. Portanto, estas dificuldades na modelagem do processamento da madeira justificam a utilização de redes neurais como ferramenta para melhoria de processo, e consequente agregação de valor ao produto final. O objetivo deste trabalho foi utilizar a capacidade de aprendizagem e a generalização das redes neurais e outras técnicas de inteligência computacional no processamento de madeira. A metodologia utilizada consistiu em utilizar redes neurais do tipo Multilayer Perceptrons (MLP) associadas à Lógica Fuzzy para construção de controlador do processo de usinagem da madeira. Adicionalmente as redes neurais realizaram uma classificação de imagens com relação aos defeitos superficiais da madeira. Foi utilizadas bases de dados obtidas através dos processos reais de usinagem da madeira. Os resultados obtidos foram satisfatórios, o que confirma que as redes neurais foram uma... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In order to obtain products and services to exceed the level of productivity required by the market, many machining wood factors should be optimized. The current goal of research in Artificial Intelligence is dedicated to develop intelligent flexible systems, self-adjusting, to decrease the presence of human operators. The control of these processes is done through the help of computer systems composed from software and hardware. The modern industrial processes are characterized by the action of various agents that are generally too complex to be represented analytically, additionally answers these systems are non-linear. Therefore, these difficulties in modeling wood machining process justify the use of Neural Network as a tool for process improvement and to add value to the final product. Computational Intelligence techniques such as Neuro-Fuzzy Networks have been proved applicable to this problem, since they combine the ability to learn from examples and to generalize the information learned from the neural network with the ability of Fuzzy Logic to turn variables into linguistic rules. The objective of this work is to use the learning ability and generalization of neural networks and other techniques of Artificial Intelligence in machining materials, which have solid non-linear character. The results were satisfactory, thus confirming the neural... (Complete abstract click electronic access below) / Doutor