An Abstract Algebraic Theory of L-Fuzzy Relations for Relational Databases

Classical relational databases lack proper ways to manage certain real-world situations including imprecise or uncertain data. Fuzzy databases overcome this limitation by allowing each entry in the table to be a fuzzy set where each element of the corresponding domain is assigned a membership degree from the real interval [0…1]. But this fuzzy mechanism becomes inappropriate in modelling scenarios where data might be incomparable. Therefore, we become interested in further generalization of fuzzy database into L-fuzzy database. In such a database, the characteristic function for a fuzzy set maps to an arbitrary complete Brouwerian lattice L. From the query language perspectives, the language of fuzzy database, FSQL extends the regular Structured Query Language (SQL) by adding fuzzy specific constructions. In addition to that, L-fuzzy query language LFSQL introduces appropriate linguistic operations to define and manipulate inexact data in an L-fuzzy database. This research mainly focuses on defining the semantics of LFSQL. However, it requires an abstract algebraic theory which can be used to prove all the properties of, and operations on, L-fuzzy relations. In our study, we show that the theory of arrow categories forms a suitable framework for that. Therefore, we define the semantics of LFSQL in the abstract notion of an arrow category. In addition, we implement the operations of L-fuzzy relations in Haskell and develop a parser that translates algebraic expressions into our implementation.

L-Fuzzy Structured Query Language

Lattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with non-comparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an L-fuzzy database is an L-fuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are non-comparable. After defining the syntax of the language formally, we provide its semantics using L-fuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.

Model for Differential Nursing Diagnosis of Alterations in Urinary Elimination Based on Fuzzy Logic

Nursing diagnoses associated with alterations of urinary elimination require different interventions, Nurses, who are not specialists, require support to diagnose and manage patients with disturbances of urine elimination. The aim of this study was to present a model based on fuzzy logic for differential diagnosis of alterations in urinary elimination, considering nursing diagnosis approved by the North American Nursing Diagnosis Association, 2001-2002. Fuzzy relations and the maximum-minimum composition approach were used to develop the system. The model performance was evaluated with 195 cases from the database of a previous study, resulting in 79.0% of total concordance and 19.5% of partial concordance, when compared with the panel of experts. Total discordance was observed in only three cases (1.5%). The agreement between model and experts was excellent (kappa = 0.98, P < .0001) or substantial (kappa = 0.69, P < .0001) when considering the overestimative accordance (accordance was considered when at least one diagnosis was equal) and the underestimative discordance (discordance was considered when at least one diagnosis was different), respectively. The model herein presented showed good performance and a simple theoretical structure, therefore demanding few computational resources.

Utilização da lógica fuzzy na geração de zonas de manejo

Este trabalho teve como objetivo utilizar a lógica fuzzy para geração de zonas de manejo, na área agrária e ambiental. Uma das aplicações consistiu da utilização do método fuzzy C-means, para geração de zonas de manejo para a cultura do mamoeiro, em um plantio comercial localizado em São Mateus-ES, com base em determinações realizadas através de amostragens e análises químicas do solo, considerando os atributos: P, K, Ca, Mg, e Saturação por bases (V%). Aplicou-se também a lógica fuzzy para desenvolver e executar um procedimento para dar suporte ao processo de tomada de decisões, envolvendo análise multicritério, gerando mapas de adequabilidade ao uso público e a conservação no Parque Estadual da Cachoeira da Fumaça, no município de Alegre-ES, considerando como fatores a localização da cachoeira, o uso do solo, os recursos hídricos, as trilhas, os locais de acessos, a infraestrutura, a declividade da área, e utilizando a abordagem de Sistema de Informações Geográficas para análise e combinação da base de dados. A partir das zonas de manejo geradas, foi possível explicar a variabilidade espacial dos atributos do solo na área de estudo da cultura do mamoeiro, e observa-se que as similaridades entre as zonas geradas, a partir de diferentes atributos, mostrou variação, mas observa-se uma influência nos dados, principalmente pelos atributos P e V. A partir do zoneamento da Unidade de Conservação foi possível selecionar áreas mais aptas ao ecoturismo, sendo encontradas próximas da cachoeira, trilhas em zonas de reflorestamento e de Mata Atlântica. Quanto às áreas propensas a medidas de conservação localizam-se próximas à cachoeira e às estruturas do parque, devido à maior pressão antrópica exercida nesses locais. Outras áreas que se destacaram, foram as áreas de pastagem, por estarem em estágio de regeneração natural. Os resultados indicam áreas de mesmo potencial de produção do mamoeiro, ou quando aplicado à área ambiental, áreas que devem receber maior cuidado para utilização por ecoturismo e para preservação e servem de base para a tomada de decisões, visando melhor aproveitamento da área.

Indoor location using fingerprinting and fuzzy logic

Indoor location systems cannot rely on technologies such as GPS (Global Positioning System) to determine the position of a mobile terminal, because its signals are blocked by obstacles such as walls, ceilings, roofs, etc. In such environments. The use of alternative techniques, such as the use of wireless networks, should be considered. The location estimation is made by measuring and analysing one of the parameters of the wireless signal, usually the received power. One of the techniques used to estimate the locations using wireless networks is fingerprinting. This technique comprises two phases: in the first phase data is collected from the scenario and stored in a database; the second phase consists in determining the location of the mobile node by comparing the data collected from the wireless transceiver with the data previously stored in the database. In this paper an approach for localisation using fingerprinting based on Fuzzy Logic and pattern searching is presented. The performance of the proposed approach is compared with the performance of classic methods, and it presents an improvement between 10.24% and 49.43%, depending on the mobile node and the Fuzzy Logic parameters.ł

SOFTWARE TO ASSESS BEEF CATTLE BODY MASS THROUGH THE FUZZY BODY MASS INDEX

ABSTRACT The Body Mass Index (BMI) can be used by farmers to help determine the time of evaluation of the body mass gain of the animal. However, the calculation of this index does not reveal immediately whether the animal is ready for slaughter or if it needs special care fattening. The aim of this study was to develop a software using the Fuzzy Logic to compare the bovine body mass among themselves and identify the groups for slaughter and those that requires more intensive feeding, using "mass" and "height" variables, and the output Fuzzy BMI. For the development of the software, it was used a fuzzy system with applications in a herd of 147 Nellore cows, located in a city of Santa Rita do Pardo city – Mato Grosso do Sul (MS) state, in Brazil, and a database generated by Matlab software.

Automação da detecção de fraudes em sistemas de medição de energia elétrica utilizando lógica fuzzy em ambiente SCADA

This works presents a proposal to make automatic the identification of energy thefts in the meter systems through Fuzzy Logic and supervisory like SCADA. The solution we find by to collect datas from meters at customers units: voltage, current, power demand, angles conditions of phasors diagrams of voltages and currents, and taking these datas by fuzzy logic with expert knowledge into a fuzzy system. The parameters collected are computed by fuzzy logic, in engineering alghorithm, and the output shows to user if the customer researched may be consuming electrical energy without to pay for it, and these feedbacks have its own membership grades. The value of this solution is a need for reduce the losses that already sets more than twenty per cent. In such a way that it is an expert system that looks for decision make with assertivity, and it looks forward to find which problems there are on site and then it wont happen problems of relationship among the utility and the customer unit. The database of an electrical company was utilized and the datas from it were worked by the fuzzy proposal and algorithm developed and the result was confirmed

Uma nova forma de calcular os centros dos Clusters em algoritmos de agrupamento tipo fuzzy c-means

Clustering data is a very important task in data mining, image processing and pattern recognition problems. One of the most popular clustering algorithms is the Fuzzy C-Means (FCM). This thesis proposes to implement a new way of calculating the cluster centers in the procedure of FCM algorithm which are called ckMeans, and in some variants of FCM, in particular, here we apply it for those variants that use other distances. The goal of this change is to reduce the number of iterations and processing time of these algorithms without affecting the quality of the partition, or even to improve the number of correct classifications in some cases. Also, we developed an algorithm based on ckMeans to manipulate interval data considering interval membership degrees. This algorithm allows the representation of data without converting interval data into punctual ones, as it happens to other extensions of FCM that deal with interval data. In order to validate the proposed methodologies it was made a comparison between a clustering for ckMeans, K-Means and FCM algorithms (since the algorithm proposed in this paper to calculate the centers is similar to the K-Means) considering three different distances. We used several known databases. In this case, the results of Interval ckMeans were compared with the results of other clustering algorithms when applied to an interval database with minimum and maximum temperature of the month for a given year, referring to 37 cities distributed across continents

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)

Kappa-PSO-ARTMAP Fuzzy: uma metodologia para detecção de intrusos baseado em seleção de atributos e otimização de parâmetros numa rede neural ARTMAP Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia

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.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and 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

Desenvolvimento de uma rede neuro-fuzzy para a previsão da carga

Pós-graduação em Engenharia Elétrica - FEIS