There have been many increasing interests in fuzzy theory in the recent years, yet there are a lot of issues to be resolved, mainly on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. This study focuses on the development of triangular fuzzy numbers, the revising of triangular fuzzy numbers, and the constructing of a HCFN (half-...

Complex numbers can capture compound features and convey multifaceted information; thereby providing means for solving complicated problems. In this paper, we combine the degree of membership and a degree of non-membership of members of Intuitionistic Fuzzy Sets via complex numbers to characterize these fuzzy sets. This approach enables extending several concepts such as classical fuzzy sets, P...

We describe fuzzy sets of type-2 and look at the definition of some of the available operations. With the aid of the extension principle we suggest some operations for implementing the concept of containment between fuzzy sets of type-2. We discuss the idea of specificity of a fuzzy set. We suggest a procedure for calculating the specificity of a interval value type-2 fuzzy subset. We show the ...

Sometimes information in a network model is based on multi-agent, multi-attribute, multi-object, multi-polar information or uncertainty rather than a single bit. An m-polar fuzzy model is useful for such network models which gives more and more precision, flexibility, and comparability to the system as compared to the classical, fuzzy and bipolar fuzzy models. In this research article, we intro...

Implicit modelling is a powerful technique to design geometric shapes, where a geometric object is described by a real function. In general, the real functions used in implicit modelling are unbounded and can take any values in space R. In general, the shapes described by different level sets of an unbounded implicit function can be varied significantly and are very unpredictable. In addition, ...

In this paper, our focus of attention is the proper propagationof fuzzy degrees in determinization of $Nondeterministic$ $Fuzzy$$Finite$ $Tree$ $Automata$ (NFFTA). Initially, two determinizationmethods are introduced which have some limitations (one inbehavior preserving and other in type of fuzzy operations). Inorder to eliminate these limitations and increasing theefficiency of FFTA, we defin...

The commonly accepted theory of fuzzy numbers (Czogała and Pedrycz, 1985) is that set up by Dubois and Prade (1978), who proposed a restricted class of membership functions, called (L,R)–numbers with shape functions L and R. However, approximations of fuzzy functions and operations are needed if one wants to follow Zadeh’s (Zadeh 1975; 1983) extension principle. It leads to some drawbacks that ...

In this paper, we extend research done in max-min fuzzy neural networks in several important ways. We replace max and min operations used in the fuzzy operations by more general t-norms and co-norms, respectively. In addition, instead of the Łukasiewicz equivalence connective used in network of ReyesGarcia and Bandler, we employ in our hybridization a variety of equivalence connectives. We expl...