in this paper we study two important concepts, i.e. the direct andthe inverse limit of hyperstructures associated with fuzzy sets of type 2, andshow that the direct and the inverse limit of hyperstructures associated withfuzzy sets of type 2 are also hyperstructures associated with fuzzy sets of type 2.

In many contexts, type-2 fuzzy sets (T2 FS) are obtained from a type-1 set to which we wish add uncertainty. However, in the current representation, there is no restriction on shape of footprint uncertainty and embedded (ESs) that can be considered acceptable. This leads, usually, loss semantic relationship between T2 FS concept it models. As consequence, interpretability some ESs explainabilit...

The use of inverse system model as a controller might be an efficient way in controlling nonlinear systems. It is also a known fact that type-2 fuzzy models can represent nonlinear systems better than type-1 fuzzy models. Therefore, an inverse type-2 fuzzy model can be used as a controller for controlling processes with nonlinearities and/or uncertainties. In the case of uncertainties and/or di...

A method for response integration in modular neural networks with type-2 fuzzy logic for biometric systems p. 5 Evolving type-2 fuzzy logic controllers for autonomous mobile robots p. 16 Adaptive type-2 fuzzy logic for intelligent home environment p. 26 Interval type-1 non-singleton type-2 TSK fuzzy logic systems using the hybrid training method RLS-BP p. 36 An efficient computational method to...

This paper proposes a new approach based on Bonferroni mean operator and possibility degree to solve fuzzy multi-attribute decision making (FMADM) problems in which the attribute value takes the form of interval type-2 fuzzy numbers. We introduce the concepts of interval possibility mean value and present a new method for calculating the possibility degree of two interval trapezoidal type-2 fuz...

Under the given system weight constraint, we consider the problem of maximizing the system lifetime and minimizing the system cost. The lifetimes of components in the system are characterized by type-2 fuzzy variables. The numbers of redundant elements of each components are the decision variables. We use the reduction methods to reduce the type-2 fuzzy lifetimes. Then, we propose a goal progra...