This paper presents a methodology for integrating common-sense and qualitative simulation of physical systems by the use of Fuzzy Sets. This allows a semi-quantitative extension to qualitative simulation that provides three significant advantages over existing techniques. Firstly, it allows a more

sets. The triangular membership functions were used to describe the membership functions. So a unique network model that captured fuzzy variables was created.

The purpose of this paper is to propose a framework based on the Fuzzy Multiple Criteria Decision Making (FMCDM) approach for ranking firms by using financial ratios. Proposed approach is based on Logarithmic Fuzzy Preference Programming (LFPP) and TOPSIS methods. LFPP method is used in determining

The control system is simulated for obtaining a desired response. The improve the vibrating system as stable one. This desired response is in this direction that the considered system will be stable.

In recent decades, several types of sets, such as fuzzy sets, interval-valued fuzzy sets, intuitionistic fuzzy sets, interval-valued intuitionistic fuzzy sets, type 2 fuzzy sets, type n fuzzy sets, and hesitant fuzzy sets, have been introduced and investigated widely. In this paper, we propose

Abstract—In this paper, the notion termed a “nonstationary fuzzy set ” is introduced, and the concept of a perturbation function that is used for generating nonstationary fuzzy sets is presented. Definitions of the basic set operators (the union, the intersection, and the complement

Intuitionistic Fuzzy Sets (IFS) are defined as pairs of mutually orthogonal fuzzy sets. We discuss this approach from an algebraic point of view. As a result we characterize two implication operators on the collection of IFS, which on a particular subset of IFS behave as a ̷Lukasiewicz and a Gödel

Multi-fuzzy set theory is an extension of fuzzy set theory, L-fuzzy set theory and Atanassov intuitionistic fuzzy set theory. In this paper we study the relation between Atanassov intuitionistic fuzzy and the proposed extension called Multi-fuzzy. Also we present the notion of Multi-fuzzy mappings

The membership of an element in a fuzzy set A is denoted by µA(x) and the fuzzy set A is defined as: A = {(x,µA(x)), x ∈X} (1) where X denotes the universe. Kaufmann [1] proposes the difference between two fuzzy sets A

Fuzzy set by Fuzzy interval is a triangular fuzzy number lying between the two specified limits. The limits to be not greater than 2 and less than-2 by fuzzy interval have been discussed in this paper. Through fuzzy interval we arrived at exactness which is a fuzzy measure and fuzzy integral