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 idea of developing a multi-joint rehabilitation robot is to satisfy the demands for recovery lower limb functionality in hemiplegic impairments and assist physiotherapists with their therapy plans. This work aims at implement Lyapunov Adaptive Swarm-Fuzzy Logic Control (LASFC) strategy 4-degree freedom (4-DoF) Lower Limb Assistive Robot (LLAR) application, which control law an integration s...

Rough set theory is an important approach to granular computing. Type-1 fuzzy set theory permits the gradual assessment of the memberships of elements in a set. Hybridization of these assessments results in a fuzzy rough set theory. Type-2 fuzzy sets possess many advantages over type-1 fuzzy sets because their membership functions are themselves fuzzy, which makes it possible to model and minim...

This study applies fuzzy set theory to the vulnerable Black-Scholes (1973) or Merton (1973) formula. Expectations of heterogeneity mean option prices are expected to be imprecise, thus making it natural to consider fuzziness to handle this. This article presents a fuzzy approach to value Black-Scholes options subject to non-identical rationality and correlated credit risk. Although no analytica...

The starting point of the paper is the (well-known)observation that the “classical” Rough Set Theory as introduced by PAWLAK is equivalent to the S5 Propositional Modal Logic where the reachability relation is an equivalence relation. By replacing this equivalence relation by an arbitrary binary relation (satisfying certain properties, for instance, reflexivity and transitivity) we shall obtain...

A rough set is a formal approximation of a crisp set which gives lower and upper approximation of original set to deal with uncertainties. The concept of neutrosophic set is a mathematical tool for handling imprecise, indeterministic and inconsistent data. In this paper, we introduce the concepts of Rough Fuzzy Neutrosophic Sets and Fuzzy Neutrosophic Rough Sets and investigate some of their pr...

The matrix model with mass term has a nontrivially classical solution which is known to represent a noncommutative fuzzy sphere. The fuzzy sphere has a lower energy then that of the trivial solution. In this letter we investigate the quantum correction of the energy of the fuzzy sphere by using the Gaussian variational technique, in contrast to the other studying in which only the small fluctua...

This paper deals with boolean linear programming problems involving coefficients in the objective function as fuzzy numbers. In the study of these problems different approaches can be proposed to use ranking fuzzy numbers methods and fuzzy preference relations obtaining auxiliary classical boolean programming problems, and to use the representation theorem obtaining a convex set with extreme po...

We propose a fuzzy-neural modeling approach for automatically constructing a fuzzy-neural model from a set of input-output data. The proposed approach consists of two phases, structure identification and parameter identification. In the structure identification phase, rough TSK fuzzy rules are extracted through a clustering algorithm. Then a fuzzy neural network is built in the parameter identi...