Top PDF Fuzzy Rule Base System for Software Classification

Fuzzy Rule Base System for Software Classification

Fuzzy Rule Base System for Software Classification

The input of the system is the OO metrics extracted from java application. The Fuzzifier calculates the matching degree of the metrics that matches the condition of the fuzzy rules. The Inference Engine calculates the rule’s conclusion based on its matching degree by using the clipping method, and combines the conclusion inferred by all fuzzy rules in a final conclusion. Finally, the Defuzzifier process coverts the fuzzy conclusion into a crisp value by using the mean of max method. There are two types of results given by the system: A fine-grained classification for each of the classes and a general classification for the java application being evaluated [11]. For this reason two sets of rules are defined: one set of rules to classify single classes and another one to evaluate the application. Moreover a decomposition tree is generated for all the classes reported during the fined-grain classification. This tree will help the developer to analyze and address classes with similar values. Unfortunately due to performance constraints only systems that report less than 200 classes will generate this similarity tree.
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Design of Fuzzy Neural Network for Function Approximation and Classification

Design of Fuzzy Neural Network for Function Approximation and Classification

Abstract — A hybrid Fuzzy Neural Network (FNN) system is presented in this paper. The proposed FNN can handle numeric and fuzzy inputs simulta- neously. The numeric inputs are fuzzified by input nodes upon presentation to the network while the fuzzy inputs do not require this translation. The connections between input to hidden nodes repre- sent rule antecedents and hidden to output nodes represent rule consequents. All the connections are represented by Gaussian fuzzy sets. The mutual subsethood measure for fuzzy sets that indicates the degree to which the two fuzzy sets are equal and is used as a method of activation spread in the network. A volume based defuzzification method is used to compute the numeric output of the network. The training of the network is done using gradient descent learning procedure. The model has been tested on three benchmark problems i.e. sine−cosine and Narazaki Ralescu’s function for approximation and Iris flower data for classification. Results are also compared with existing schemes and the proposed model shows its natural capability as a function approximator, and classifier.
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A Evaluation of Software Re-Usability Using Software Metrics  through Fuzzy Logic

A Evaluation of Software Re-Usability Using Software Metrics through Fuzzy Logic

Implementing a fuzzy system requires that the different categories of the different inputs be represented by fuzzy sets which, in turn, is represented by membership functions. The domain of membership function is fixed, usually the set of real numbers, and whose range is the span of positive numbers in the closed interval [0, 1]. There are total 11 membership functions available in Mat Lab. We considered Triangular Membership Functions (TMF) for our problem, because of its simplicity and heavy use by researchers for prediction models [18]. It is a three-point function, defined by minimum α, maximum β and modal value m i.e. TMF (α, m, β), where (α ≤ m ≤ β). This process is known as fuzzification. These membership functions are then processed in fuzzy domain by inference engine based on knowledge base (rule base and data base) supplied by domain experts and finally the process of converting back fuzzy numbers into single numerical values is called defuzzification [17].
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Geração genética de bases de conhecimento fuzzy: novas perspectivas

Geração genética de bases de conhecimento fuzzy: novas perspectivas

an input values in each fuzzy set defining the given attribute. This way, a confidence degree is calculated for each rule. This confidence degree is used by the classification process and gives credibility to the final classi- fication. Notice that, for a well defined fuzzy data base, each input value presents membership degrees greater than zero in at most two fuzzy sets, i.e., the two corresponding branches of the test node are fired simulta- neously. On the other hand, if the decision tree is large, the inference process might require considerable computational effort when compared to the classic C4.5, since more branches are fired. This issue can be soft- ened by defining a minimum threshold of membership degree in order to continue testing the rules or not.
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Sistema genético difuso para a predição da frequência respiratória de pintinhos sujeitos a desafios térmicos

Sistema genético difuso para a predição da frequência respiratória de pintinhos sujeitos a desafios térmicos

The methodology used for genetic adjustment of the fuzzy system was proposed by, Pires (2004), Valdez et al. (2011), Starczewski et al. (2014), Georgieva (2016) and Tan et al. (2016). In this methodology, there is a knowledge base to which a genetic tuning process is applied to improve system performance without changing the basis of existing rules. This is done in order to adjust the fuzzy system parameters to improve their performance by adjusting the relevance functions and output variables. The method consists of two steps. The first is the generation of rules by a method driven by data for the rapid construction of the rule base, focusing on simplicity and transparency of the rules. The second relates to the genetic optimization of system performance by tuning the membership functions.
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Proposal and Implementation of MPLS Fuzzy Traffic Monitor

Proposal and Implementation of MPLS Fuzzy Traffic Monitor

An appropriate decision of traffic splitting is performed among the computed number of LSPs required by Traffic Splitting Algorithm (TSA). The computed fuzzy based LSPs for forwarding packets are obtained to avoid the situation of underutilization and over utilization of paths. None of the paths remains idle for longer time and proper utilization of resources takes place. Hence, it is better for congestion to be prevented rather than corrected. Implementation of FL using Mamdani Fuzzy Inference System [31] evaluates final decisions as LsS_value and TSS_value. The available number of rules in the Rule Base matrix LsS and TSS represents intermediate situations and provides the control mechanism with a highly dynamic action.
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An Application of Fuzzy Inference System Composed of Double-Input Rule Modules to Control Problems

An Application of Fuzzy Inference System Composed of Double-Input Rule Modules to Control Problems

First, we perform two-category classification problems as in Fig.2 to investigate the basic feature of the proposed method and to compare it with the SIRMs model. In the classification problems, points on [0, 1] × [0, 1] × [0, 1] are classified into two classes: class 0 and class 1. The class boundaries are given as spheres centered at (0.5, 0.5, 0.5). For Sphere, the inside of sphere is associated with class 1 and the outside with class 0. For Double-Sphere, the area between Spheres 1 and 2 is associated with class 1 and the other area with class 0. For triple-Sphere, the inside of Sphere1 and the area between Sphere2 and Sphere3 is associated with class 1 and the other area with class 0. The desired output y r
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PHONETIC CLASSIFICATION BY ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM AND SUBTRACTIVE CLUSTERING

PHONETIC CLASSIFICATION BY ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM AND SUBTRACTIVE CLUSTERING

The identification of fuzzy model consists of two major phases: structure identification and parameter optimization. The first phase is the determination of number of fuzzy if-then rules and membership functions of the premise fuzzy sets while the second phase is the tuning of the parameter values of the fuzzy model [11]. However, there will be two problems if we directly use the traditional Takagi Sugeno model for the speech recognition system. The first one is the network reasoning will fail if the input dimension is too large. The second problem is that with the increase of input dimension, rule numbers will have an exponential growth and cause “rule disaster”. Thus, determination of an appropriate structure becomes an important issue, then clustering techniques are applied to solve this problem. [12] [13] [14].
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FUZZY RULE-BASED SYSTEM FOR EVALUATION OF UNCERTAINTY TRANSACTION IN CASSAVA CHAIN

FUZZY RULE-BASED SYSTEM FOR EVALUATION OF UNCERTAINTY TRANSACTION IN CASSAVA CHAIN

RESUMO: Este trabalho apresenta um sistema baseado em regras fuzzy que resultou em um índice que indica o nível de incerteza relacionada com as transações comerciais entre produtores de mandioca e de seus compradores, com base na abordagem da Economia dos Custos de Transação. O sistema fuzzy foi desenvolvido utilizando como variáveis de entrada "Previsão de Demanda/compra", "Previsão de Produção" e "Compartilhamento de informações acerca de inovações de produção". A variável de saída consiste no nível de incerteza da transação entre vendedor e agente comprador, que pode servir como um sistema para a detecção de ineficiências. Entrevistas realizadas com 27 produtores de mandioca registrados nos Escritórios de Desenvolvimento Regionais de Tupã e Assis, São Paulo, Brasil, e 48 de seus compradores sustentaram o desenvolvimento do sistema. O modelo matemático aplicado na avaliação destes produtores indicou que 55% dos produtores têm relações comerciais com nível de incerteza Muito Alta, 25% Média ou Alta; e os demais, classificados entre Baixa ou Média. A partir dos resultados encontrados, simulações de ações poderiam ser implementadas para reduzir o grau de incerteza e os custos de transação envolvidos.
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OPTIMAL RULE SELECTION BASED DEFECT CLASSIFICATION SYSTEM USING NAÏVE BAYES CLASSIFIER

OPTIMAL RULE SELECTION BASED DEFECT CLASSIFICATION SYSTEM USING NAÏVE BAYES CLASSIFIER

Defects are commonly defined as deviations from or expectations that might lead to failures in operation for the software quality [9]. Software defect is a deficiency in a software product that causes it to perform unexpectedly. From a software user’s perspective, a defect is anything that causes the software not to meet their expectations. In this context, a software user can be either a person or another piece of software [1]. A defect is any blemish, imperfection, or undesired behavior that occurs either in the deliverable or in the product. Anything related to defect is a continual process and not a state [3].Software defects are expensive. Moreover, the cost of finding and correcting defects represents one of the most expensive software activities. It is well known that software production organizations spend a sizeable amount of their project budget to rectify the defects introduced in to the software systems during development process.
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CAMILA DOS SANTOS SANT’ANNA TEORIA DOS CONJUNTOS FUZZY: DA SIMULAÇÃO AO LETRAMENTO PARA ALUNOS DO ENSINO MÉDIO

CAMILA DOS SANTOS SANT’ANNA TEORIA DOS CONJUNTOS FUZZY: DA SIMULAÇÃO AO LETRAMENTO PARA ALUNOS DO ENSINO MÉDIO

In this work we present a research carried out in the classroom with the objective of treating subjective questions through the Fuzzy Logic with students of the third year of high school. We begin with the study of the basic concepts of Fuzzy Set Theory and systems based on fuzzy rules. Then, aiming at an application, we developed a fuzzy system to evaluate the risk of an individual developing type 2diabetes, from risk factors. Finally, we propose a didactic sequence to the students, through a questionnaire and a lecture given by the author. The results of the research are presented and, if on the one hand they indicate the difficulty of the students with the fuzzy reasoning, on the other hand, demonstrated the interest and the role of Logica Fuzzy, and therefore, of Mathematics in solving problems of our daily life.
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J. Braz. Comp. Soc.  vol.6 número3

J. Braz. Comp. Soc. vol.6 número3

Abstract This paper considers a GIS methodological framework based on fuzzy sets theory for land use management. Some principles of development of the GIS methodological framework are formu- lated. Applications of the GIS methodological framework are designed. In particular GIS knowledge management fuzzy models for analysis of soil commutative contamination by heavy metals, for the study of soil acidity, and for evaluation of soil conservation actions are obtained.

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A Presença de Alcalóides em Espécies Botânicas da Amazônia - Menispermaceae.

A Presença de Alcalóides em Espécies Botânicas da Amazônia - Menispermaceae.

An Integrated System of Classification of Flowering Plamts.. Boletim do IMPA[r]

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FUZZY RULE-BASED SYSTEM FOR AVENUE MANAGEMENT

FUZZY RULE-BASED SYSTEM FOR AVENUE MANAGEMENT

The relation between mutual fund performance and fund characteristics is of much interest to financial market practitioners and investors. However, there is a lack of conclusive knowledge on this issue. This study introduces a method which examines the relation between fund returns and fund asset size, loads, expense ratios and turnover. The study focuses developing Techno-Portfolio Advisor which provides investment options and optimal funds for achieving their objectives. The system is developed based on the fuzzy inference rule. The system fulfills the investor’s objectives and preferences in terms rate of return, risk and asset allocation and diversification in order to reach an optimum solution. Therefore, Techno-Portfolio Advisor provides a solid support for decision making in mutual fund investment. The study also creates awareness among the investor community in choosing the best mutual fund scheme.
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A method for solving fully fuzzy linear system with trapezoidal fuzzy numbers

A method for solving fully fuzzy linear system with trapezoidal fuzzy numbers

One field of applied mathematics that has many applications in various areas of science is solving a system of linear equations. Systems of simultaneous linear equations play a major role in various areas such as operational research, physics, statistics, engineering and social sciences. When the estimation of the system coefficients is imprecise and only some vague knowledge about the actual values of the parameters is available, it may be convenient to represent some or all of them with fuzzy numbers [22]. Fuzzy number arithmetic is widely applied and useful in computation of linear system whose parameters are all or partially represented by fuzzy numbers.
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A Discernibility Degree and Rough Set Based Classification Rule Generation Algorithm (RGD)

A Discernibility Degree and Rough Set Based Classification Rule Generation Algorithm (RGD)

(3)The rules in LEM2 have bigger mean coverage, but longer mean length and lower mean accuracy than RGD. The bigger mean coverage because LEM2 select the attribute values with biggest coverage to construct a rule. The longer mean length and lower mean accuracy is due to that LEM2 use the formula (1) as the stop schema when forming a rule, and as a result for an inconsistent example the rule's length is as long as the example itself, and the final classification performance is worsened.

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Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System

Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System

In this paper, by combining the Backstepping method and the T-S fuzzy model method, the fuzzy backstepping sliding mode control was presented for a class of mismatched uncertain system. In this new method, nonlinear system is described based on T-S fuzzy dynamical model, and the nonlinear system is translated into local linear model by fuzzy method, not only make the system is stable, and avoid the chattering phenomenon of sliding mode control, Then the global stability of the control law was also achieved by a Lyapunov function. Simulation results indicate that the proposed controller is valid and effective, and it has strong robustness to external disturbance and the performance of system.
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USING AN INTEGRATED FUZZY INFERENCE SYSTEM AND ARTIFICIAL NEURAL NETWORK TO FORECAST DAILY DISCHARGE

USING AN INTEGRATED FUZZY INFERENCE SYSTEM AND ARTIFICIAL NEURAL NETWORK TO FORECAST DAILY DISCHARGE

(7) With the classification results from the previous procedure, the forecast outflow discharge was found by fuzzy inference. The model was named Gauss. Fuzzy inference w[r]

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Combination of Neural Networks and Fuzzy Clustering Algorithm to Evalution Training Simulation-Based Training

Combination of Neural Networks and Fuzzy Clustering Algorithm to Evalution Training Simulation-Based Training

Abstract —With the advancement of computer technology, computer simulation in the field of education are more realistic and more effective. The definition of simulation is to create a virtual environment that accurately and real experiences to improve the individual. So Simulation Based Training is the ability to improve, replace, create or manage a real experience and training in a virtual mode. Simulation Based Training also provides large amounts of information to learn, so use data mining techniques to process information in the case of education can be very useful. So here we used data mining to examine the impact of simulation-based training. The database created in cooperation with relevant institutions, including 17 features. To study the effect of selected features, LDA method and Pearson's correlation coefficient was used along with genetic algorithm. Then we use fuzzy clustering to produce fuzzy system and improved it using Neural Networks. The results showed that the proposed method with reduced dimensions have 3% better than other methods.
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Uma abordagem matemática para auxiliar o diagnóstico de demências: tratando incertezas...

Uma abordagem matemática para auxiliar o diagnóstico de demências: tratando incertezas...

O modelo de ´arvore decis´oria para o tipo de demˆencia foi refinado com uma base de dados de 49 casos. Para essa avalia¸c˜ao foi criada uma tabela que cont´em informa¸c˜oes como tipo de in´ıcio (Mem´oria ou inicio insidioso), achados na neuroimagem (imagem caracter´ıstica da doen¸ca), velocidade da evolu¸c˜ao (evolu¸c˜ao r´apida ou lenta) entre outros crit´erios. O especialista avaliou o caso e escolheu marcando um X na op¸c˜ao que ele acredita ser a hip´otese diagn´ostica daquele caso. A figura 3.16 apresenta um exemplo do formul´ario modelo utilizado para testes dos caminhos do diagrama de decis˜ao.
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