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A demanda crescente na complexidade, eficiência e confiabilidade nos sistemas industriais modernos têm estimulado os estudos da teoria de controle aplicada no desenvolvimento de sistemas de Monitoração e Detecção de Falhas. Neste trabalho foi desenvolvida uma metodologia inédita de Monitoração e Detecção de Falhas através do algoritmo GMDH e Redes Neurais Artificiais (RNA) que foi aplicada ao reator de pesquisas do IPEN, IEA-R1. O desenvolvimento deste trabalho foi dividido em duas etapas: sendo a primeira etapa dedicada ao pré-processamento das informações, realizada através do algoritmo GMDH; e a segunda o processamento das informações através de RNA. O algoritmo GMDH foi utilizado de duas maneiras diferentes: primeiramente, o algoritmo GMDH foi utilizado para gerar uma melhor estimativa da base de dados, tendo como resultado uma matriz denominada matriz_z, que foi utilizada no treinamento das RNA. Logo após, o GMDH foi utilizado no estudo das variáveis mais relevantes, sendo estas variáveis utilizadas no processamento das informações. Para realizar as simulações computacionais, foram propostos cinco modelos: Modelo 1 (Modelo Teórico) e Modelos 2, 3, 4 e 5 (Dados de operação do reator). Após a realização de um estudo exaustivo dedicado a Monitoração, iniciou-se a etapa de Detecção de Falhas em sensores, onde foram simuladas falhas na base de dados dos sensores. Para tanto as leituras dos sensores tiveram um acréscimo dos seguintes valores: 5%, 10%, 15% e 20%. Os resultados obtidos utilizando o algoritmo GMDH na escolha das melhores variáveis de entrada para as RNA foram melhores do que aqueles obtidos utilizando apenas RNA, o que viabiliza o uso da nova metodologia de Monitoração e Detecção de Falhas em sensores apresentada. / The increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix_z, which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors.

A demanda crescente na complexidade, eficiência e confiabilidade nos sistemas industriais modernos têm estimulado os estudos da teoria de controle aplicada no desenvolvimento de sistemas de Monitoração e Detecção de Falhas. Neste trabalho foi desenvolvida uma metodologia inédita de Monitoração e Detecção de Falhas através do algoritmo GMDH e Redes Neurais Artificiais (RNA) que foi aplicada ao reator de pesquisas do IPEN, IEA-R1. O desenvolvimento deste trabalho foi dividido em duas etapas: sendo a primeira etapa dedicada ao pré-processamento das informações, realizada através do algoritmo GMDH; e a segunda o processamento das informações através de RNA. O algoritmo GMDH foi utilizado de duas maneiras diferentes: primeiramente, o algoritmo GMDH foi utilizado para gerar uma melhor estimativa da base de dados, tendo como resultado uma matriz denominada matriz_z, que foi utilizada no treinamento das RNA. Logo após, o GMDH foi utilizado no estudo das variáveis mais relevantes, sendo estas variáveis utilizadas no processamento das informações. Para realizar as simulações computacionais, foram propostos cinco modelos: Modelo 1 (Modelo Teórico) e Modelos 2, 3, 4 e 5 (Dados de operação do reator). Após a realização de um estudo exaustivo dedicado a Monitoração, iniciou-se a etapa de Detecção de Falhas em sensores, onde foram simuladas falhas na base de dados dos sensores. Para tanto as leituras dos sensores tiveram um acréscimo dos seguintes valores: 5%, 10%, 15% e 20%. Os resultados obtidos utilizando o algoritmo GMDH na escolha das melhores variáveis de entrada para as RNA foram melhores do que aqueles obtidos utilizando apenas RNA, o que viabiliza o uso da nova metodologia de Monitoração e Detecção de Falhas em sensores apresentada. / The increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix_z, which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors.

Tato práce se snaží nabídnout odpověď na otázku, zda má smysl při rozhodování o budoucím pohybu měnových kurzů brát ohled na výsledky vystupující z modelů získaných analýzou měnových kurzů a relevantních časových řad provedeného pomocí metod strojového učení. Účelem této práce je tak prozkoumat možnosti analýzy kurzů (ve formě časových řad) s důrazem na použití nových metod spočívajících svým těžištěm v oblasti umělé inteligence a strojového učení (neuronové sítě, algoritmus GMDH sítí).

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This diploma thesis is about design and realization of neural network MIA GMDH for ekonomical modelling by inductive method. Models are compared with statistical methods by quallity and usebility degree. An application was developed for verification of functionality on experiments. The same experiments were run in econometrical software. The results were compared. The MIA GMDH is suitable for economic modelling.

During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results. / by Jay Thakkar. / Pagination error. "References" should be leaves 63-67, and pagination end with leaf 67. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

An ecosystem always involves some implicit relations between habitat environment and inhabitants, whose reciprocal links can not be identified easily. Three sets of ecological monitoring data were analyzed in this study, including coral reef, algae (Thalassia hemprichii Aschers) in Kenting National Park, and Formosan landlocked salmon (Oncorhynchus masou formosanus) in the basin of Chichiawan Stream. Two data-oriented analysis techniques, which are Habitat Evaluation Procedure (HEP) and Group Method of Data Handling (GMDH), were applied to retrieve the embedded patterns from these data sets. Eventually, for each data set, a forecasting model based on the technique of combined forecasting were developed, which is to integrate the results from HEP and GMDH, for improving the overall modeling precision. The results of this study show that the data-oriented analyses, such as HEP and GMDH, are useful for finding valid information from the ecological data. Furthermore, the combined forecasting technique can really improve the performance of model prediction even for the ecological research. In order to acquire the most important habitat environmental factors affecting the inhabitants, this study also performed sensitivity analysis of the models. The contributions of this study are to identify effective knowledge for future ecological research and to provide reasonable suggestions for formulating conservation strategy.

Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay. / by Mukul Gupta. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Discharge of brine wastewater produced from industrial plants into adjacent coastal water bodies is considered as a preferable and common method currently used in many offshore industrial plants. Therefore, it is important to carefully study the behavior of jets and their environmental impacts on water bodies close to the discharge points, especially when the density is different between the jets and the receiving water. The main goal of this study is to improve the understanding of the mixing behaviour of jet trajectories for positively (offset) and negatively (inclined) buoyant jets when density is considered a significant factor, and also to examine the accuracy of some RANS turbulence models and one type of artificial neural network in predicting jet trajectory behaviours. In the first part of this study, experiments using a PIV system for offset buoyant jets were conducted in order to study the effect of the density differences (due to salinity [nonthermal] or temperature [thermal]) between the discharge and the receiving water body on the jet behavior, and the results showed that the nonthermal jets behaved differently as compared to the thermal jets, even though the densimetric Froude numbers (Frd) and density differences (∆ρ) were similar. In addition, a Reynolds-averaged Navier-Stokes (RANS) numerical model was performed using open-source CFD code (OpenFOAM) with a developed solver (modified form of the pisoFoam solver). The realizable k-ε model showed the best prediction among the models. Secondly, an extensive experimental study of an inclined dense jet for two angles (15°and 52°) was conducted to study the effect of these angles on the jets’ geometrical characteristics in the presence of a wide range of densimetric Froude numbers as well as with different discharge densities. More experimental data were obtained for these angles to be added to the previous data for the purpose of calibrating, validating, and comparing the various numerical models for future studies. The results of these experiments are used to evaluate the performance of a type of artificial neural network method called the group method of data handling (GMDH), and the GMDH results are then compared with existing analytical solutions in order to prove the accuracy of the GMDH method in simulating mixing behaviors in water bodies. Thirdly, a comprehensive study on predicting the geometrical characteristics of inclined negatively-buoyant jests using GMDH approach was conducted. The superiority of this model was demonstrated statistically by comparing to several previous analytical models. The results obtained from this study confirm that the GMDH model was highly accurate and was the best among others for predicting the geometrical characteristics of inclined negatively-buoyant jests.