chaotic systems are nonlinear dynamic systems, the main feature of which is high sensitivity to initial conditions. to initiate a design process in fuzzy model, chaotic systems must first be represented by t-s fuzzy models. in this paper, a new fuzzy modeling method based on sector nonlinearity approach has been recommended for chaotic systems relating to initial condition variations using the ...

The centroid of an interval type-2 fuzzy set (IT2 FS) provides a measure of the uncertainty of such a FS. Its calculation is very widely used in interval type-2 fuzzy logic systems. In this paper, we present properties about the centroid of an IT2 FS. We also illustrate many of the general results for a T2 fuzzy granule (FG) in order to develop some understanding about the uncertainty of the FG...

This paper investigates optimal control problem for discrete-time interval type-2 (IT2) fuzzy systems with poles constraint. An IT2 fuzzy controller is characterized by two predefined functions, and the membership functions and the premise rules of the IT2 fuzzy controller can be chosen freely. The pole assignment is considered, which is constrained in a presented disk region. Based on Lyapunov...

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 (IT2) membership functions. Although they can be used produce systems with enhanced explainability, so far, the latter comes at expense of high computational cost. Specifically, exhaustive type-reduction method for CIT2 Mamdani has shown too slow in practical applications and ...

Abstract—This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homoge...

TYPE-2 fuzzy sets (T2 FSs), originally introduced by Zadeh [3], provide additional design degrees of freedom in Mamdani and TSK fuzzy logic systems (FLSs), which can be very useful when such systems are used in situations where lots of uncertainties are present [4]. The implementation of this type-2 FLS involves the operations of fuzzification, inference,and output processing. We focus on ―outp...

This article addresses the event-triggered asynchronous fault detection (FD) problem of fuzzy-model-based nonlinear Markov jump systems (MJSs) with partially unknown transition probabilities. For this objective, plant is modeled as an interval type-2 (IT2) fuzzy MJS aid IT2 sets capturing uncertainties membership functions. An adaptive scheme introduced to bring down costs communication network...

This paper develops a PI-like interval type-2 (IT2) fuzzy maximum power point tracking (MPPT) control method for a class of wind power generation and battery charging systems with DC/DC buck converters. After the brief model descriptions of the wind power turbine, generator and DC/DC buck converter, the proposed MPPT controller is designed using IT2 fuzzy control logics and its asymptotical sta...

The purpose of the present work is to establish a one-to-one correspondence between the family of interval type-2 fuzzy reflexive/tolerance approximation spaces and the family of interval type-2 fuzzy closure spaces.

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...