In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum deg...

Description logics are a popular, and widely used, formalism for describing domain knowledge in a structured way. To cope with the vagueness of real-world knowledge, several fuzzy description logics have already been proposed. In this contribution, we argue that, rather than modelling all vagueness in a generalized description logic, it may be more natural to encode vague attributes in concrete...

Fuzzy control systems have had various applications in a wide range of science and engineering in recent years. Since an unstable control system is typically useless and potentially dangerous, stability is the most important requirement for any control system (including fuzzy control system). Conceptually, there are two types of stability for control systems: Lyapunov stability (a special case ...

Many-Valued logics have been developed to represent mathematical model of imprecision, vagueness, uncertainty and ambiguity in the information. In real world each and every species is vague, human knowledge and the natural languages have a bunch of vagueness or imprecise information. This paper attempts to present three main theories of many-valued logics to treat the vagueness: Fuzzy Logic, Va...

The paradigm of deterministic nite-state automata (DFAs) and their corresponding regular languages have been shown to be very useful for addressing fundamental issues in recurrent neural networks. The issues that have been addressed include knowledge representation, extraction, and reenement as well development of advanced learning algorithms. Recurrent neural networks are also very promising t...

We discuss an ontological framework for approximation, i.e., to approximation of concepts and vague dependencies specified in a given ontology. The presented approach is based on different information granule calculi. We outline the rough–fuzzy approach for approximation of concepts and vague dependencies.

Classical warranty plans require crisp data obtained from strictly controlled reliability tests. However, in a real situation these requirements might not be fulfilled. In an extreme case, the warranty claims data come from users whose reports are expressed in a vague way. Furthermore, there are special situations where several characteristics are used together as criteria for judging the warra...