Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the anteced...

This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and repr...

image segmentation is an essential issue in image description and classification. currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. moreover, there are many uncertainties and vagueness in images, which crisp clustering and even type-1 fuzzy clustering could not handle. hence, type-...

The paper presents the theory of control systems for electrical drives based on fuzzy logic, from the author’s point of view. The paper offers practical answers at questions related to the design of speed control systems based on fuzzy PI speed controller: what is the adequate structure of fuzzy controller, the number of rules fuzzy values, how to assure stability, implementation, control syste...

2 Vagueness, Uncertainty and Fuzzy Logic 2 2.1 Vagueness and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Vagueness and Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 What Is Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 Agenda of Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . ....

Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some refinements, they may not, in general, be good approximators for adaptive systems. By extending fuzzy sets to qualitative fuzzy sets, fuzzy logic may become universal approximators for adaptive systems. Similar considerations can be extended to neural networks.

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other rece...

After a basic introduction of fuzzy logic, we discuss its role in artiicial and computational intelligence. Then we present innovative applications of fuzzy logic, focusing on fuzzy expert systems, with one typical example explored in some detail. The article concludes with suggestions how artiicial intelligence and fuzzy logic can beneet from each other.