A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Forming the crucial point in fuzzy modeling, the fuzzy model identiication procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, to provide the parameters of the fuzzy model. To enhance the eeciency of the fuzzy mode...

A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Representing the crucial point in fuzzy modeling, the fuzzy model identiication procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, providing the parameters of the fuzzy model. To enhance the eeciency of the fuzzy ...

Conceptual data modeling has become essential for non-traditional application areas. Some conceptual data models have been proposed as tools for database design and object-oriented database modeling. Information in real-world applications is often vague or ambiguous. Currently, a little research is underway on modeling the imprecision and uncertainty in conceptual data modeling and the conceptu...

Fuzzy Decision Tree induction is a powerful methodology to extract human interpretable classification rules. The induction of fuzzy decision tree is done using popular Fuzzy ID3 algorithm [1, 2]. The other parameters which influence the performance of fuzzy decision tree are cut standard parameter  and leaf selection threshold parameter th  . Generally these parameters are to be selected heur...

For modeling of change of terminal server load, the approach including representation of time series of server parameters in the form of fuzzy time series is used. Further in the article the analysis of fuzzy time series is considered. The model of fuzzy tendencies is offered for terminal server traffic modeling. This model reflects change of the volume of terminal server traffic expressed ling...

The most important challenges in electric load forecasting is to find the accurate electricity load forecasting. Because, it is volatile in nature and has to be consumed immediately. Fuzzy Decision Tree is applied to predict the annual electricity requirement in India. Population and Per Capital gross domestic product (GDP) are taken as input variables and the electricity consumption is predict...

The purpose of this paper is to study the description of power system QV curves by using the method of fuzzy modeling for analysis of voltage stability. Voltage instability of power system comes from increasing load rapidly, and causes bus voltage to drop. When voltage is out of control and it can be voltage collapse. The QV curve can identify voltage stability limit, and it determine robustnes...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

For winning in global competition, companies need to recognition and monitoring of customer's behavior to forecast their behavior and desires earlier than competitors. This research tries to recognize the attributes which lead to customer churn. For this, behavior of 3150 subscribers of an Iranian mobile operator, has observed during one year and trends of them has analyzed by a customized LLNF...

with respect to the importance of commercial alliances in airline hypercompetitive environment, partner performance evaluation is a critical matter before making any decision about partnership. in this article, utilizing fuzzy theory, a hybrid airline evaluation model is developed in a way that all airline performance strategic dimensions are considered, as well as, it can deal with qualitative...