The task of finding shortest paths in weighted graphs is one of the archetypical problems encountered in the domain of combinatorial optimization and has been studied intensively over the past five decades. More recently, fuzzy weighted graphs, along with generalizations of algorithms for finding optimal paths within them, have emerged as an adequate modeling tool for prohibitively complex and/...

The mathematical framework for studying of a fuzzy approximate reasoning is presented in this paper. Two important defuzzification methods (Area defuzzification and Height defuzzification) besides the center of gravity method which is the best well known defuzzification method are described. The continuity of the defuzzification methods and its application to a fuzzy feedback control are discus...

In the paper basic defuzzification techniques are considered. The defuzzification process is present at a fuzzy system when an output fuzzy set should be mapped to a crisped value. Features are given which are the base for a defuzzification techniques comparison. Techniques are classified into several groups. The overview of defuzzification techniques is given, as well as their comparison on th...

Evolutionary Adaptive Defuzzification Methods are a kind of defuzzification methods based on using a parametrical defuzzification expression tuned with evolutionary algorithms. Their goal is to increase the accuracy of the fuzzy system without loosing its interpretability. They induce a kind of rule cooperation in the defuzzification interface. 1 This paper deals with Evolutionary Adaptive Defu...

This paper proposes financial time-series forecasting using a feature selection method based on the non-overlap area distribution measurement method supported in a neural network with weighted fuzzy membership functions (NEWFM) and the TakagiSugeno (T-S) fuzzy model. The non-overlap area distribution measurement method selects the minimum number of features with the highest performance by remov...

Defuzzification is the process of converting a fuzzy quantity to precise quantity, just as fuzzification is the conversion of a precise quantity to a fuzzy quantity. Various types of defuzzification methods, are available for conversion of fuzzy to non-fuzzy. In this paper, defuzzification functions in intuitionistic fuzzy environment such as triangular, trapezoidal, L-trapezoidal, R-trapezoida...

A novel technique of designing application specific defuzzification strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzification approximator is validated by showing the convergence when approximating several existing defuzzification strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. T...

Defuzzification converts the final fuzzy output set of fuzzy controller and fuzzy inference systems to a significant crisp value. However, there are various mathematical methods for defuzzification, but there is not any certain systematic method for choosing the best strategy. In this paper, first we explain the structure of a fuzzy inference system and then after a short review of defuzzificat...

Defuzzification is one of the fundamental steps in the development of fuzzy knowledge based systems. Given a fuzzy set μ over the reference set X, defuzzification applied to μ returns an element of X. While a large number of methods exists for the case of X being a numerical scale, only few methods are applicable when X corresponds to a categorical scale. Aggregation procedures have been extens...

AbstractIn general, expert rules expressed by imprecise (fuzzy) words of natural language like “small” lead to control recommendations. If we want design an automatic controller, need, based on these fuzzy recommendations, generate a single value. A procedure for such generation is known as defuzzification. The most widely used defuzzification centroid defuzzification, in which, the desired val...