A fuzzy inference system for identification of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behavior. In this paper, a system based on fuzzy logic systems is developed to overcome the problems usually found in the conventional mathematical models. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the fuzzy approach. Simulation results are presented to justify the validity of the proposed approach.

Mapping lightning processes using fuzzy inference system

This paper describes a novel approach for mapping lightning processes using fuzzy logic. The core regarding lightning process is to identify and to model those uncertain information on mathematical principles. In fact, the lightning process involves several nonlinear features that our current mathematical tools would not be able to model. The estimation process has been carried out using a fuzzy system based on Sugeno's architecture. Simulation results confirm that proposed approach can be efficiently used in these types of problem.

Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.

A proposal for reliability evaluation of components on electric power distribution system integrating probabilistic models and fuzzy inference systems

The system reliability depends on the reliability of its components itself. Therefore, it is necessary a methodology capable of inferring the state of functionality of these components to establish reliable indices of quality. Allocation models for maintenance and protective devices, among others, have been used in order to improve the quality and availability of services on electric power distribution systems. This paper proposes a methodology for assessing the reliability of distribution system components in an integrated way, using probabilistic models and fuzzy inference systems to infer about the operation probability of each component. © 2012 IEEE.

Inferência fuzzy para o problema de corte de estoque com sobras aproveitáveis de material

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivalued adaptive neuro-fuzzy controller for robot vehicle - art. no. 1406

Robotic vehicle navigation in unstructured and uncertain environments is still a challenge. This paper presents the implementation of a multivalued neurofuzzy controller for autonomous ground vehicle (AGVs) in indoor environments. The control system consists of a hierarchy of mobile robot using multivalued adaptive neuro-fuzzy inference system behaviors.

Development of a water quality index using a fuzzy logic: A case study for the sorocaba river

Due to growing urbanization and industrialization, the environment is suffering from pollution of rivers, degradation of soils and deteriorated air quality. Quality indices appear to be useful to evaluate the conditions of these media. The aim of this study was the development of a water quality index using a fuzzy inference system, since such an approach has proved advantageous in addressing problems that are subjective by nature or for which the data are uncertain. The methodology employed was based on this inference system, and considered the nine water quality parameters employed by CETESB (Companhia de Tecnologia de Saneamento Ambiental, São Paulo, Brazil) to evaluate water quality. After assessment of the data using the index, a comparison was made with the WQI (Water Quality Index), which is used for the monitoring of various water bodies, including in the study region. The results obtained using the index developed on the basis of fuzzy inference were found to be more useful than those derived from the method currently used by CETESB, since losses and/or omissions concerning individual parameters were minimized. © 2010 IEEE.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Classificação da curvatura de vertentes em perfil via Thin Plate Spline e Inferência Fuzzy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação fuzzy de vertentes por krigagem e TPS com agregação de regiões via diagrama de Voronoi

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Aplicação de um sistema de Inferência Fuzzy de suporte à decisão para estimação de valores de ações cotadas na Bolsa de Valores de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimação da densidade de solos utilizando sistemas de inferência fuzzy

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

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

Pós-graduação em Engenharia Elétrica - FEIS

Fuzzy Index for Public Supply Water Quality

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.