155 resultados para Fuzzy ranking


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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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In the world stage, the environmental condition has been a big public policies issue. A clear understanding of the parameters that determine the state of the environment is essential for estimation of the quality of life of the population, not least since ecosystems are highly complex. Consequently, definition of public policies demands the use of evaluation tools that can combine and quantify information in a clear way. The use of indicators and indices that are able to translate the complexity of environmental conditions in cities into simpler terms has been increasingly effective in decision-making, since they assist in general evaluation of the situation in question, identification of priority actions and anticipation of future trends. In an attempt to evaluate the environmental conditions of the Brazilian city, Sorocaba, an Environmental Quality Fuzzy Index (IFQAmb) was proposed. In this work this methodology is improved. After reviewing the IFQAmb methodology, a number of changes in the index are proposed. Additional variables are suggested, derived from a State Environment Department program whose objective is to grant municipalities the title of Municipio Verde e Azul (Green and Blue City). In addition, a new rule base is being drafted to enable consideration of all possibilities, since in the existing version the use of specific criteria eliminates a significant number of rules. The changes seek to define with clarity and precision the conceptual aspects and structure of the IFQAmb, so that it can provide an even more effective evaluation of environmental performance, guiding future actions in order to improve the living conditions of the population of Sorocaba.

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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 work presents a strategy to control nonlinear responses of aeroelastic systems with control surface freeplay. The proposed methodology is developed for the three degrees of freedom typical section airfoil considering aerodynamic forces from Theodorsen's theory. The mathematical model is written in the state space representation using rational function approximation to write the aerodynamic forces in time domain. The control system is designed using the fuzzy Takagi-Sugeno modeling to compute a feedback control gain. It useds Lyapunov's stability function and linear matrix inequalities (LMIs) to solve a convex optimization problem. Time simulations with different initial conditions are performed using a modified Runge-Kutta algorithm to compare the system with and without control forces. It is shown that this approach can compute linear control gain able to stabilize aeroelastic systems with discontinuous nonlinearities.

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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.

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

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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

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Pós-graduação em Ciências Cartográficas - FCT

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

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

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The information retrieval process involves subjective, imprecise and vague concepts, such as "information need", "relevance", and the very concept of "information". The main information retrieval models treat these concepts accurately, represented by a single numerical value. The fuzzy logic, while operating with the uncertainty of natural phenomena in a systematic and rigorous manner, represents a promising alternative to solve some problems related to information retrieval. This paper presents the fuzzy logic and some examples of its use in information retrieval systems (IRS).

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This paper proposes a Fuzzy Goal Programming model (FGP) for a real aggregate production-planning problem. To do so, an application was made in a Brazilian Sugar and Ethanol Milling Company. The FGP Model depicts the comprehensive production process of sugar, ethanol, molasses and derivatives, and considers the uncertainties involved in ethanol and sugar production. Decision-makings, related to the agricultural and logistics phases, were considered on a weekly-basis planning horizon to include the whole harvesting season and the periods between harvests. The research has provided interesting results about decisions in the agricultural stages of cutting, loading and transportation to sugarcane suppliers and, especially, in milling decisions, whose choice of production process includes storage and logistics distribution. (C)2014 Elsevier B.V. All rights reserved.