886 resultados para Optimization. Markov Chain. Genetic Algorithm. Fuzzy Controller
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Engenharia Mecânica - FEG
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Agronomia (Energia na Agricultura) - FCA
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Pós-graduação em Engenharia Elétrica - FEIS
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Pós-graduação em Engenharia Elétrica - FEIS
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Engenharia Mecânica - FEG
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Engenharia Mecânica - FEG
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The present work develops a model to simulate the dynamics of a quadcopter being controlled by a PD fuzzy controller. Initially is presented a brief history of quadcopters an introduction to fuzzy logic and fuzzy control systems. Afterwards is presented an overview of the quadcopter dynamics and the mathematical modelling development applying Newton-Euler method. Then the modelling are implemented in a Simulink model in addition to a PD fuzzy controller. A prototype proposition is made, by describing each necessary component to build up a quadcopter. In the end the results from the simulators are discussed and compared due to the discrepancy between the model using ideal sensor and the model using non-ideal sensors
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In this paper we deal with the one-dimensional integer cutting stock problem, which consists of cutting a set of available objects in stock in order to produce ordered smaller items in such a way as to optimize a given objective function, which in this paper is composed of three different objectives: minimization of the number of objects to be cut (raw material), minimization of the number of different cutting patterns (setup time), minimization of the number of saw cycles (optimization of the saw productivity). For solving this complex problem we adopt a multiobjective approach in which we adapt, for the problem studied, a symbiotic genetic algorithm proposed in the literature. Some theoretical and computational results are presented.
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The main objective of the presented study is the design of a analog multiplier-divider as integrant part of the type-reducer circuit of type-2 fuzzy controller chip. The proposed circuit is a multiplier/divider which operates in current mode, in the CMOS technology with a supply voltage of 1.8 V.The circuit simulation was performed in PSPICE software with simulation model provided by AMS (Austria Mikro Systems International) in CMOS technology 0.35μm
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This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions.