the purpose of this paper is to introduce the concept of l-fuzzybilinear operators. we obtain a decomposition theorem for l-fuzzy bilinearoperators and then prove that a l-fuzzy bilinear operator is the same as apowerset operator for the variable-basis introduced by s.e.rodabaugh (1991).finally we discuss the continuity of l-fuzzy bilinear operators.

Abstract Real-world problems often deal with uncertainties related to imprecision and vagueness, for which fuzzy modeling has provided a successful approach. Ranking numbers is necessary complex task in multiple processes, such as decision making. To facilitate the of ranking numbers, it been introduced an approach construct distances from classical interval because their α-cuts are continuous ...

In this paper fuzzy piecewise cubic interpolation is constructed for fuzzy data based on B-spline basis functions. We add two new additional conditions which guarantee uniqueness of fuzzy B-spline interpolation.Other conditions are imposed on the interpolation data to guarantee that the interpolation function to be a well-defined fuzzy function. Finally some examples are given to illustrate the...

This paper is an attempt to develop quantitative domain theory over frames. Firstly, we propose the notion of a fuzzy basis, and several equivalent characterizations of fuzzy bases are obtained. Furthermore, the concept of a fuzzy algebraic domain is introduced, and a relationship between fuzzy algebraic domains and fuzzy domains is discussed from the viewpoint of fuzzy basis. We finally give a...

There are some difficulties encountered in the application of fuzzy Radial Basis Function (RBF) neural network. One of them is how to determine the number of hidden rule neurons and another difficulty is about interpretability. In order to overcome these difficulties, we have proposed a fuzzy neural network based on RBF network and takagi-sugeno fuzzy system. We have used a new structure of fuz...

This paper proposes a Single Input Neuro-fuzzy Logic Controller based on Radial Basis Function Network (SI-NFRBFN) for non-linear systems. To obtain single input from multi inputs a Distance Method is suggested. Using this method all the uncertain inputs are simplified into a single input known as distance. With the help of this variable the control unit matrix introduced in Hybrid Neuro-fuzzy ...

Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuuy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the vie...