In this paper, we extend research done in max-min fuzzy neural networks in several important ways. We replace max and min operations used in the fuzzy operations by more general t-norms and co-norms, respectively. In addition, instead of the Łukasiewicz equivalence connective used in network of ReyesGarcia and Bandler, we employ in our hybridization a variety of equivalence connectives. We expl...

In this paper, the texture property “coarseness” is modeled by means of type-2 fuzzy sets, relating representative coarseness measures (our reference set) with the human perception of this texture property. The type-2 approach allows to face both the imprecision in the interpretation of the measure value and the uncertainty about the coarseness degree associated with a measure value. In our stu...

The reduction of noise in an image, considered as a goal itself or as a pre-processing step, is an important issue. Besides classical filters, a wide variety of fuzzy filters has been developed. These filters use techniques from fuzzy set theory, and have the ability to incorporate the uncertainty that is involved in noise detection. However, it is very difficult to judge the quality of these f...

An adaptive fuzzy logic controller is described which assists the driver in vehicle speed and distance control by offering a driving strategy via an active accelerator and brake pedal. In order to be accepted by the driver, the behavior of the system called ACC (Adaptive Cruise Control) has to meet the expectations of the human driver to a certain degree. The Fuzzy-ACC provides separate control...

In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuz...

In this paper we describe a new control laboratory course at The Ohio State University. Students execute a series of laboratory exercises, for a variety of processes, implementing fuzzy control, adaptive fuzzy control, and other intelligent control techniques, with a particular focus on fuzzy control. Fully instrumented independent testbeds in the laboratory emphasize several sensing and actuat...

Sorting algorithms are common tools for manipulating data and used in both standalone circumstances and within larger more complex algorithms. Thus, it is highly desirable for sorting algorithms to be efficient in terms of storage and computation. By applying the concept of fuzzy logic (an abstract version of Boolean logic) to any well-known algorithm, it generates an abstract version (i.e., fu...

The conventional fuzzy set model uses the MIN and MAX operators for the evaluation of boolean operators AND and OR, respectively. It has been criticized to generate incorrect ranked output in many cases. This is because the MIN and MAX operators do not well correspond with human behavior for document ranking. There are a variety of fuzzy operators to replace the MIN and MAX operators. In this p...

This paper presents an implementation of fuzzy-logic controller to solve the unit-commitment and economic dispatch problem of thermal generation plant with the main objective of evaluating the total minimum operating cost for each loading condition while subjected to a variety of constraints. The model in this study contains five generation units and the daily load demand is subdivided into 12 ...

Ontology consistency has been shown to be undecidable for a wide variety of fairly inexpressive fuzzy Description Logics (DLs). In particular, for any t-norm “starting with” the Lukasiewicz t-norm, consistency of crisp ontologies (w.r.t. witnessed models) is undecidable in any fuzzy DL with conjunction, existential restrictions, and (residual) negation. In this paper we show that for any t-norm...