66 resultados para Adaptive neuro-fuzzy inference system


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Pós-graduação em Engenharia Elétrica - FEIS

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This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H-1 and H-2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H-1 and H-2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H-1 and H-2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H-1, while for H-2, it was 0.23%; indicating the system efficiency in decision-making.

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

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

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An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Engenharia Mecânica - FEB

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Supervising and controlling the many processes involved in petroleum production is both dangerous and complex. Herein, we propose a multiagent supervisory and control system for handle continuous processes like those in chemical and petroleum industries In its architeture, there are agents responsible for managing data production and analysis, and also the production equipments. Fuzzy controllers were used as control agents. The application of a fuzzy control system to managing an off-shore installation for petroleum production onto a submarine separation process is described. © 2008 IEEE.

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This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Human population growth and increased industrial activity in recent decades have contributed to a range of environmental problems, including the contamination of groundwater and surface water. In order to help in the management of these resources, water quality indices are used as tools to summarize multiple parameters and express them in the form of a single number. The ability to provide both an integrated assessment of changes in environmental variables, as well as performance tracking, has resulted in such indices being increasingly employed in surface water monitoring programs. The aim of this study was to develop an Index for Public Supply Water Quality (IPS) using a fuzzy inference methodology. Linguistic systems generally provide satisfactory tools for qualitative purposes, enabling the inclusion of descriptive variables with reduced loss of individual information. Validation of the technique was achieved by analysis of measurement data obtained for the Sorocaba River, provided by CETESB. The new procedure proved more rigorous, compared to classical IPS. It could be readily applied in the evaluation of other water bodies, or be adjusted to incorporate additional parameters also considered important for the assessment of water quality.

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This paper presents the application and use of a methodology based on fuzzy theory and simulates its use in intelligent control of a hybrid system for generating electricity, using solar energy, photovoltaic and wind. When using a fuzzy control system, it reached the point of maximum generation of energy, thus shifting all energy generated from the alternative sources-solar photovoltaic and wind, cargo and / or batteries when its use not immediately. The model uses three variables used for entry, which are: wind speed, solar radiation and loading the bank of batteries. For output variable has to choose which of the batteries of the battery bank is charged. For the simulations of this work is used MATLAB software. In this environment mathematical computational are analyzed and simulated all mathematical modeling, rules and other variables in the system described fuzzy. This model can be used in a system of control of hybrid systems for generating energy, providing the best use of energy sources, sun and wind, so we can extract the maximum energy possible these alternative sources without any prejudice to the environment.

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Pós-graduação em Engenharia Mecânica - FEG