We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on “output processing,” which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more in...

it is firstly proved that the multi-input-single-output (miso) fuzzy systems based on interval-valued $r$- and $s$-implications can approximate any continuous function defined on a compact set to arbitrary accuracy.  a formula to compute the lower upper bounds on the number  of interval-valued fuzzy sets needed to achieve a pre-specified approximation  accuracy for an arbitrary multivariate con...

In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithm...

osting by E Abstract Direct adaptive fuzzy controller is a class of adaptive fuzzy controllers which use fuzzy logic system (FLS) as controller. Interval type-2 fuzzy sets are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of fuzzy logic controller (FLC). In this paper, a direct adaptive interval type-2 FLC is proposed for controllin...

Controller design remains an elusive and challenging problem for uncertain nonlinear dynamics. Interval type-2 fuzzy logic systems (IT2FLS) in comparison with type-1 fuzzy logic systems claim to effectively handle system uncertainties especially in the presence of disturbances and noises, but lack a formal mechanism to guarantee performance. In contrast, adaptive sliding mode control (ASMC) pro...

The objective of this paper is to present an approach that utilizes an interval type-2 fuzzy decision making system (IT2FDMS) to quantify the Project Management Efficiency (PME). The algorithm developed in this paper is based upon interval type-2 fuzzy logic, giving it the ability to solve complex problems plagued with uncertainty and vagueness. A interval type-2 fuzzy decision making system is...