Fuzzy subgroups and T -vague groups are interesting fuzzy algebraic structures that have been widely studied. While fuzzy subgroups fuzzify the concept of crisp subgroup, T -vague groups can be identified with quotient groups of a group by a normal fuzzy subgroup and there is a close relation between both structures and T -indistinguishability operators (fuzzy equivalence relations). In this pa...

This paper is a reply to Laviolette and Seaman’s critical discussion of fuzzy set theory. Rather than questioning the interest of the Bayesian approach to uncertainty, some reasons why Bayesian find the idea of a fuzzy set not palatable are laid bare. Some links between fuzzy sets and probability that Laviolette and Seaman seem not to be aware of are pointed out. These links suggest that, contr...

This study develops a fuzzy sliding mode controller (FSMC) based on a variable boundary layer. A fuzzy inference mechanism is used to on-line tune the thickness of the boundary layers of the controller. Minimum rule base has been used for the fuzzy inference system which results in low calculation effort. The aim of this paper is to design a controller which will eliminate the chattering of FSM...

A Real-Time Optimization (RTO) strategy incorporating the fuzzy sets theory is developed, where the problem constraints obtained from process considerations are treated in fuzzy environment. Furthermore, the objective function is penalized by a fuzzified form of the key process constraints. To enable using conventional optimization techniques, the resulting fuzzy optimization problem is the...

We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System (ANFIS). We examine the period 1950-2009, where we take as the in-sample or training period 1950-2005, ...

Fuzzy System (FS) and Artificial Neural Networks (ANN) are complementary methods. But while ANN can learn from data FS cannot. Also, Genetic Algorithms (GA) are complementary to FS. While the FS are easy to understand, the GA are not, although they have the ability to learn, and so on. Many researches have been devoted to its fusion. Our purpose is to give a survey of these questions. 2000 Math...

Fuzzy reasoning based on fuzzy logic was first proposed by Zadeh to handle the vague concept of reasoning in our day to day life. Since the inception of fuzzy reasoning several extensions, specifications and applications on fuzzy reasoning have been developed by different researchers. Fuzzy reasoning is now a very well established method and has a tremendous potentiality in different applicatio...

Data-driven fuzzy modeling has been used in a wide variety of applications. However, in fuzzy rule-based models acquired from numerical data, redundancy often exists in the form of redundant rules or similar fuzzy sets. This results in unnecessary structural complexity and decreases the interpretability of the system. In this paper, a rule-base self-extraction and simpli&cation method is propos...

Fuzzy control is based on fuzzy logic, which provides a most efficient way to handle inexact information as a basis of reasoning. With fuzzy logic it is possible to convert knowledge, which is expressed in an uncertain form, to an exact and precise output for uncertain systems. In fuzzy control, the controller can be simply represented by if-then-else rules. The interpretation of the controller...

BACKGROUND Driving under the influence (DUI) of alcohol is a major public health concern, and many individuals continue to drink and drive even after being convicted of a DUI offense. Latinos, in particular, are disproportionately likely to be arrested for a DUI, have higher rates of recidivism, and are more likely to die in alcohol-related accidents than non-Latino Whites. Latinos also experie...