Since Lotfi A. Zadeh introduced the concept of fuzzy sets in 1965, many authors have devoted their efforts to the study of these new sets, both from a theoretical and applied point of view. Fuzzy sets were later extended in order to get more adequate and flexible models of inference processes, where uncertainty, imprecision or vagueness is present. Type 2 fuzzy sets comprise one of such extensi...

In computer-based search systems, similarity plays a key role in replicating the human process which underlies many natural abilities, such as image recovery, language comprehension, decision-making, or pattern recognition. The for images consists of establishing correspondence between available and those sought by user, measuring images. fact, per content is generally based on visual character...

The design of weight is one of the important parts in fuzzy decision making, as it would have a deep effect on the evaluation results. Entropy is one of the weight measure based on objective evaluation. Non--probabilistic-type entropy measures for fuzzy set and interval type-2 fuzzy sets (IT2FS) have been developed and applied to weight measure. Since the entropy for (IT2FS) for decision making...

Fuzziness (entropy) is a commonly used measure of uncertainty for type-1 fuzzy sets. For interval type-2 fuzzy sets (IT2 FSs), centroid, cardinality, fuzziness, variance and skewness are all measures of uncertainties. The centroid of an IT2 FS has been defined by Karnik and Mendel. In this paper, the other four concepts are defined. All definitions use a Representation Theorem for IT2 FSs. Form...

The Fuzzy Analytic Network Process (ANP) is generally used for solving multicriteria decision making (MCDM) problems by considering the pairwise comparison between criteria/sub-criteria, and inner/outer dependencies among criteria. Linguistic expressions are used for experts’ judgements, and these judgements are imprecise and vague. Hence, incorporating fuzziness with multi-criteria decision ma...

Recurrent fuzzy neural networks (FNNs) have been widely applied to dynamic system processing problems. However, most recurrent FNNs focus on the use of type-1 fuzzy sets. This paper proposes a Mamdani-type recurrent interval type-2 FNN (M-RIT2FNN) that uses interval type-2 fuzzy sets in both rule antecedent and consequent parts. The reason for using interval type-2 fuzzy sets is to increase net...