Fuzzy logic techniques are efficient in solving complex, ill-defined problems that are characterized by uncertainty of environment and fuzziness of information. Fuzzy logic allows handling uncertain and imprecise knowledge and provides a powerful framework for reasoning. Fuzzy reasoning models are relevant to a wide variety of subject areas such as engineering, economics, psychology, sociology,...

Following the development of fuzzy logic theory by Lotfi Zadeh, its applications were investigated by researchers in different fields. Presenting and working with uncertain data is a complex problem. To solve for such a complex problem, the structure of relationships and operators dependent on such relationships must be repaired. The fuzzy database has integrity limitations including data depen...

Fuzzy numbers are fuzzy subsets of the set of real numbers satisfying some additional conditions. Fuzzy numbers allow us to model very difficult uncertainties in a very easy way. Arithmetic operations on fuzzy numbers have also been developed, and are based mainly on the crucial Extension Principle. When operating with fuzzy numbers, the results of our calculations strongly depend on the shape ...

This paper presents Fuzzy Cognitive Maps as an approach in modeling the behavior and operation of complex systems. This technique is the fusion of the advances of the fuzzy logic and cognitive maps theories, they are fuzzy weighted directed graphs with feedback that create models that emulate the behavior of complex decision processes using fuzzy causal relations. There are some applications in...

The characteristics of the new version of the fuzzy systems development environment Xfuzzy is presented. The environment covers the aspects related to the specification, verification, adjustment and implementation of fuzzy systems. It is an open environment (in the sense that the user can define many functional and structural aspects) and a free distribution tool that allows proving new formali...

One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature. We will call these approaches as basic refinement approaches. In this work, we present a short study of how these b...

This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces f...

In this paper a total of 16 variables, which are most influential on surface roughness in grinding, are considered. The variables are classified into three groups depending on their significance and effect on surface roughness. A three-layer fuzzy model is used to correlate these variables to surface roughness using the fuzzy rules generated based on experimental observations and recommendation...