This article analyses the algorithm for Medical Diagnostic Reasoning based on the new Similarity measure of Fuzzy sets. The new Similarity measure is based on the fact that Fuzzy membership function and Fuzzy membership value for the complement of a Fuzzy set are two different things. In this paper, the analysis of the algorithm is carried out using Big O notation to find the order of growth fo...

As a combination of the hesitant fuzzy set (HFS) and the single valued neutrosophic set (SVNS), the single valued neutrosophic hesitant fuzzy set (SVNHFS) is an important concept to handle uncertain and vague information existing in real-life, which consists of three membership functions including hesitancy, as the truth-hesitancy membership function, the indeterminacy-hesitancy membership func...

A novel formalism is presented, which enables the processing of heterogeneous data including numeric-, interval-valued-, or fuzzydata. The data in question are represented herein as interval-supported fuzzy sets with suitable membership functions. The term Fuzzy Interval Number (FIN) denotes one of the aforementioned types of data. A FIN can have either a positive or a negative membership funct...

The goal of the fuzzy diatoms models is to use the advancement of the fuzzy theory and with predictive modelling to predict the outcome of given condition of diatoms. Fuzzy modelling depends from many factors, mainly by the shape of the membership functions, similarity metrics and above all the operation between the fuzzy sets – the fuzzy operators. In this paper we introduce new fuzzy operator...

Fuzzy sets and logic have been largely used for the treatment of the uncertainty, vagueness and ambiguity found in the modeling of real problems. However, there may exist also the case when there is uncertainty related to the membership functions to be used in the modeling of fuzzy sets and fuzzy numbers, as there are many ways to define the shape of this kind of number. Thus, one can use, for ...

Fuzzy sets refer to the datasets which do not have separate, distinct clusters, and they contain data elements whose membership values are between 0 and 1. Often each element belongs to several groups. We design a disk diagram with an augmented frequency graph to visualize elements in a fuzzy set. It shows the distribution of the elements within the set with respect to the membership function v...

in this paper, we introduce a takagi-sugeno (ts) fuzzy model which is derived from a typical multi-layer perceptron neural network (mlp nn). at first, it is shown that the considered mlp nn can be interpreted as a variety of ts fuzzy model. it is discussed that the utilized membership function (mf) in such ts fuzzy model, despite its flexible structure, has some major restrictions. after modify...

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the app...

In many applications of fuzzy set theory, the membership of an object is not defined directly. One of its attributes (like height, age, weight, . . . ) is first mapped on a real number (often by means of a physical instrument) and a parametric function then maps this real number on a membership degree in some fuzzy set (like ‘tall’, ‘old’, ‘heavy’, . . . ). A very common parametric function is ...

the linear multiobjective transportation problem is a special type of vector minimum problem in which constraints are all equality type and the objectives are conicting in nature. this paper presents an application of fuzzy goal programming to the linear multiobjective transportation problem. in this paper, we use a special type of nonlinear (hyperbolic and exponential) membership functions to ...