Previsão de carga multinodal usando a rede neural ART-ARTMAP fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Determinação de indicadores de eficiência e racionalidade energética na produção de etanol com a aplicação da lógica fuzzy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de um modelo fuzzy para determinação do calor latente com aplicação em sistemas de irrigação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelo fuzzy para obtenção do tempo de aeração em silos verticais visando reduzir o consumo de energia elétrica

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Estimação da densidade de solos utilizando sistemas de inferência fuzzy

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Sistema fuzzy de suporte a decisão para aplicação de programa de eficiência energética em serrarias

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Lógica Fuzzy aplicada ao controle de um sistema híbrido de geração de energia elétrica: eólica, fotovoltaica e biogás

Pós-graduação em Engenharia Mecânica - FEG

Desenvolvimento de aplicações em medicina e agronomia utilizando lógica fuzzy e neuro fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy

Currently new techniques for data processing, such as neural networks, fuzzy logic and hybrid systems are used to develop predictive models of complex systems and to estimate the desired parameters. In this article the use of an adaptive neuro fuzzy inference system was investigated to estimate the productivity of wheat, using a database of combination of the following treatments: five N doses (0, 50, 100, 150 and 200 kg ha(-1)), three sources (Entec, ammonium sulfate and urea), two application times of N (at sowing or at side-dressing) and two wheat cultivars (IAC 370 and E21), that were evaluated during two years in Selviria, Mato Grosso do Sul, Brazil. Through the input and output data, the system of adaptive neuro fuzzy inference learns, and then can estimate a new value of wheat yield with different N doses. The productivity prediciton error of wheat in function of five N doses, using a neuro fuzzy system, was smaller than that one obtained with a quadratic approximation. The results show that the neuro fuzzy system is a viable prediction model for estimating the wheat yield in function of N doses.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimating outcomes in newborn infants using fuzzy logic

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

On Switched Regulator Design of Uncertain Nonlinear Systems Using Takagi-Sugeno Fuzzy Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimação do risco de morte de recém-nascidos utilizando logica fuzzy tipo-2 intervalar

The main objective of the presented study is the development of a predictive interval type-2 fuzzy inference system in order to estimate the mortality risk for a newborn, to be used as an auxiliary tool for decision making in medical centers where there is a lack of professionals for this purpose and, afterwards, to compare its performance to a type-1 fuzzy system. The input variables were chosen due to their acquisition ‘simplicity, not involving any invasive tests, such as blood tests or other specific tests. The variables are easily obtained in the first few minutes of life: birth weight, gestational age at delivery, 5-minute Apgar score and previous report of stillbirth. Databases from the DATASUS were used to validate the model. 1351 records from the city of São José dos Campos, a mid-sized city in the São Paulo state’s countryside, were considered in this study. Finally, an analysis using the ROC curve was performed to estimate the model’s accuracy

Estimação do número de internações por doenças respiratórias utilizando lógica fuzzy tipo-2 intervalar

In this work was developed a fuzzy computational model type-2 predictive interval, using the software of the type-2 fuzzy MATLAB toolbox, the final idea is to estimate the number of hospitalizations of patients with respiratory diseases. The interest in the creation of this model is to assist in decision makeshift hospital environment, where there are no medical or professional equipment available to provide the care that the population need. It began working with the study of fuzzy logic, the fuzzy inference system and fuzzy toolbox. Through a real database provided by the Departamento de Informática do Sistema Único de Saúde (DATASUS) and Companhia de Tecnologia de Saneamento Básico (CETESB), was possible to start the model. The analyzed database is composed of the number of patients admitted with respiratory diseases a day for the public hospital in São José dos Campos, during the year 2009 and by factors such as PM10, SO2, wind and humidity. These factors were analyzed as input variables and, through these, is possible to get the number of admissions a day, which is the output variable of the model. For data analysis we used the fuzzy control method type-2 Mamdani. In the following steps the performance developed in this work was compared with the performance of the same model using fuzzy logic type-1. Finally, the validity of the models was estimated by the ROC curve

Controle fuzzy de um motor dc utilizando sistema de aquisição de dados e o matlab

The present work develops a fuzzy inference system to control the rotation speed of a DC motor available in Degem Kit. Therefore, it should use the fuzzy toolbox of Matlab in conjunction with the data acquisition board NI - USB - 6009, a National Instrument’s board. An introduction to fuzzy logic, the mathematical model of a DC motor and the operation of data acquisition board is presented first. Followed by the controller fuzzy model implemented using Simulink which is described in detail. Finally, the prototype is shown and the simulator results are presented