Fuzzy logic to predict thermal damages of ground parts

One of the critical problems in implementing an intelligent grinding process is the automatic detection of workpiece surface burn. This work uses fuzzy logic as a tool to classify and predict burn levels in the grinding process. Based on acoustic emission signals, cutting power, and the mean-value deviance (MVD), linguistic rules were established for the various burn situations (slight, intermediate, severe) by applying fuzzy logic using the Matlab Toolbox. Three practical fuzzy system models were developed. The first model with two inputs resulted only in a simple analysis process. The second and third models have an additional MVD statistic input, associating information and precision. These two models differ from each other in terms of the rule base developed. The three developed models presented valid responses, proving effective, accurate, reliable and easy to use for the determination of ground workpiece burn. In this analysis, fuzzy logic translates the operator's human experience associated with powerful computational methods.

Fuzzy environmental quality index

This paper presents the construction of a fuzzy environmental quality index for decision support in municipal environmental management. Five groups of indicators were selected in order to obtain an equation that best represented reality in terms of environmental quality. The calculation was carried out using fuzzy mathematical concepts, with the aid of the package Fuzzy Logical Toolbox 2.1 for Matlab ® 6.1, which provides functions and some applications of the theory of fuzzy sets. The work seeks to create a method of inference concerning the nature of urban areas that are unsustainable with respect to the environment, an issue that is often relegated to the background during public policy discussions. The development of this index, together with its implementation and dissemination, could improve public awareness of environmental issues, and promote mobilization towards the use of best practices in local development. © 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.

Uma aplicação da lógica Fuzzy

Pós-graduação em Matemática Universitária - IGCE

Aspectos lógicos e algébricos dos conjuntos fuzzy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Teoria de conjuntos fuzzy e aplicações

Pós-graduação em Matemática Universitária - IGCE

Proposta de metodologia de auxílio à decisão utilizando conjuntos Fuzzy para repotenciação de PCHs

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelagem fuzzy aplicada a políticas públicas: proposta de um indice municipal de qualidade ambiental

Pós-graduação em Ciências Ambientais - Sorocaba

On the Newton method for solving fuzzy optimization problems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Lógica Fuzzy aplicada à geologia

Pós-graduação em Matemática em Rede Nacional - IBILCE

Metodologia de determinação de funções de pertinência de controla-dores fuzzy para a avaliação energética de empresas de avicultura de postura

The objective of this work is to determine the membership functions for the construction of a fuzzy controller to evaluate the energy situation of the company with respect to load and power factors. The energy assessment of a company is performed by technicians and experts based on the indices of load and power factors, and analysis of the machines used in production processes. This assessment is conducted periodically to detect whether the procedures performed by employees in relation to how of use electricity energy are correct. With a fuzzy controller, this performed can be done by machines. The construction of a fuzzy controller is initially characterized by the definition of input and output variables, and their associated membership functions. We also need to define a method of inference and a processor output. Finally, you need the help of technicians and experts to build a rule base, consisting of answers that provide these professionals in function of characteristics of the input variables. The controller proposed in this paper has as input variables load and power factors, and output the company situation. Their membership functions representing fuzzy sets called by linguistic qualities, as “VERY BAD” and “GOOD”. With the method of inference Mandani and the processor to exit from the Center of Area chosen, the structure of a fuzzy controller is established, simply by the choice by technicians and experts of the field energy to determine a set of rules appropriate for the chosen company. Thus, the interpretation of load and power factors by software comes to meeting the need of creating a single index that indicates an overall basis (rational and efficient) as the energy is being used.

Fuzzy linguistic model for bioelectrical impedance vector analysis

Backgrounds Ea aims: The boundaries between the categories of body composition provided by vectorial analysis of bioimpedance are not well defined. In this paper, fuzzy sets theory was used for modeling such uncertainty. Methods: An Italian database with 179 cases 18-70 years was divided randomly into developing (n = 20) and testing samples (n = 159). From the 159 registries of the testing sample, 99 contributed with unequivocal diagnosis. Resistance/height and reactance/height were the input variables in the model. Output variables were the seven categories of body composition of vectorial analysis. For each case the linguistic model estimated the membership degree of each impedance category. To compare such results to the previously established diagnoses Kappa statistics was used. This demanded singling out one among the output set of seven categories of membership degrees. This procedure (defuzzification rule) established that the category with the highest membership degree should be the most likely category for the case. Results: The fuzzy model showed a good fit to the development sample. Excellent agreement was achieved between the defuzzified impedance diagnoses and the clinical diagnoses in the testing sample (Kappa = 0.85, p < 0.001). Conclusions: fuzzy linguistic model was found in good agreement with clinical diagnoses. If the whole model output is considered, information on to which extent each BIVA category is present does better advise clinical practice with an enlarged nosological framework and diverse therapeutic strategies. (C) 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.

FORA - A Fuzzy Set Based Framework for Online Reputation Management

The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly exhibit a disastrous influence on the online reputation of organizations. Based on social Web data, this study describes the building of an ontology based on fuzzy sets. At the end of a recurring harvesting of folksonomies by Web agents, the aggregated tags are purified, linked, and transformed to a so-called fuzzy grassroots ontology by means of a fuzzy clustering algorithm. This self-updating ontology is used for online reputation analysis, a crucial task of reputation management, with the goal to follow the online conversation going on around an organization to discover and monitor its reputation. In addition, an application of the Fuzzy Online Reputation Analysis (FORA) framework, lesson learned, and potential extensions are discussed in this article.

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.