The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

Approximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then
rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) …

In this first chapter of Part I, we discuss families of fuzzy implications obtained from binary
operations on the unit interval [0, 1], ie, from basic fuzzy logic connectives, viz., t-norms, t …

Co** with uncertainty in dynamical systems has recently received some attention in
artificial intelligence (AI), particularly in the fields of qualitative and model-based reasoning …

Intelligence systems. We perfonn routine tasks on a daily basis, as for example:• recognition
of faces of persons (also faces not seen for many years),• identification of dangerous …

A complex fuzzy set is a fuzzy set whose membership function takes values in the unit circle
in the complex plane. This paper investigates various operation properties and proposes a …

Fuzzy rule interpolation (FRI) offers an effective approach for making inference possible in
sparse rule-based systems (and also for reducing the complexity of fuzzy models). However …

Many search strategies have been exploited for the task of feature selection (FS), in an effort
to identify more compact and better quality subsets. Such work typically involves the use of …

The concept of fuzzy rule interpolation in sparse rule bases was introduced in 1993. It has
become a widely researched topic in recent years because of its unique merits in the topic of …

Fuzzy control is at present still the most important area of real applications for fuzzy theory. It
is a generalized form of expert control using fuzzy sets in the definition of vague/linguistic …