Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2009, Iranian Journal of Fuzzy Systems
The operations in the set of fuzzy numbers are usually obtained by the Zadeh extension principle. But these definitions can have some disadvantages for the applications both by an algebraic point of view and by practical aspects. In fact the Zadeh multiplication is not distributive with respect to the addition, the shape of fuzzy numbers is not preserved by multiplication, the indeterminateness of the sum is too increasing. Then, for the applications in the Natural and Social Sciences it is important to individuate some suitable variants of the classical addition and multiplication of fuzzy numbers that have not the previous disadvantage. Here, some possible alternatives to the Zadeh operations are studied.
1994
Two dierent denitions of a Fuzzy number may b e found in the literature. Both fulll Goguen's Fuzzication Principle but are dierent in nature because of their dierent starting points. The rst one was introduced b y Z adeh and has well suited arithmetic and algebraic properties. The second one, introduced by Gantner, Steinlage and Warren, i s a good and formal representation of the concept from a topological point of view. The objective of this paper is to analyze these denitions and discuss their main features.
Kybernetika
Institute of Mathematics of the Czech Academy of Sciences provides access to digitized documents strictly for personal use. Each copy of any part of this document must contain these Terms of use.
Results in Control and Optimization, 2023
Fuzzy set theory is a generalized form of crisp set theory where elements are binary inclusion forms. In fuzzy set, it differs with degree of membership for every element in the set. There are several strategies for arithmetic operations on fuzzy numbers. Previous studies show that there are many approaches, such as the α-cut technique, extension principle, vertex method, etc., to execute arithmetic operations on fuzzy numbers. In this study we perform details analysis and interpretation on arithmetic operations based on the α-cut method in a new way.
The objective of this paper is to develop arithmetic operations between generalized trape-zoidal (triangular) fuzzy numbers so that the drawbacks of the existing works are overcome. In this respect, the extension principle has been used to calculate diierent arithmetic op-erations.
Fuzzy Sets and Systems, 2015
An alternative look on fuzzy numbers based on two random variables and their summation and multiplication is introduced. This approach to summation covers the standard Zadeh's extension principle and triangular norm-based approach, however, it is more general. We illustrate it on some examples. Moreover, copula-based summation preserving the class of triangular (trapezoidal) fuzzy numbers is discussed.
Mathematics and Statistics, 2022
In this paper, a new hypothesis of fuzzy number has been proposed which is more precise and direct. This new proposed approach is considered as an equivalence class on set of real numbers 𝑅 with its algebraic structure and its properties along with theoretical study and computational results. Newly defined hypothesis provides a well-structured summary that offers both a deeper knowledge about the theory of fuzzy numbers and an extensive view on its algebra. We defined field of newly defined fuzzy numbers which opens new era in future for fuzzy mathematics. It is shown that, by using newly defined fuzzy number and its membership function, we are able to solve fuzzy equations in an uncertain environment. We have illustrated solution of fuzzy linear and quadratic equations using the defined new fuzzy number. This can be extended to higher order polynomial equations in future. The linear fuzzy equations have numerous applications in science and engineering. We may develop some iterative methods for system of fuzzy linear equations in a very simple and ordinary way by using this new methodology. This is an innovative and purposefulness study of fuzzy numbers along with replacement of this newly defined fuzzy number with ordinary fuzzy number.
Journal of Management and Science, 2017
This paper introduced a new conception Intuitionistic Decagonal fuzzy Number and defines fundamental arithmetic operations like addition, subtraction. Numerical examples for addition and subtraction between two Intuitionistic Decagonal fuzzy Numbers are given.Score function and accuracy function are also defined.
This paper introduces new operations on fuzzy numbers and intervals. These operations allow keeping the shape of a membership function intact and constructing complex linguistic terms corresponding to such linguistic hedges as "very" and "more or less". The article contains mathematical equations which allow us to determine the characteristic points of operation results for particular types of membership functions without integral evaluation.
International Mathematical Forum, 2008
In this paper we present two theorems that rely on the Zadeh's extension principle. These two theorems can be used to define a crisp function on a given fuzzy real number. And this will produce a new fuzzy real number. Using this, we can define some special fuzzy numbers such as: square root, natural logarithm, logarithm,... etc.
IEEE Transactions on Fuzzy Systems
To solve problems from the area of Computing with Words [12], , arithmetic operations often have to be carried out on fuzzy numbers. The author proposes to call fuzzy numbers fuzzy sets of numbers (FSofN) to emphasize the fact that they are sets of many numbers (in the continuous case of infinitely many numbers) and not one, single number as the name fuzzy number suggests, because it occasionally leads to incorrect interpretations of their concept. To realize arithmetic operations on FSofN the standard extension principle is used. This principle was intended for possibilistic FSofN. However, in practical tasks many FSofN are of probabilistic character. If for such FSofN the standard extension principle is used the results will be incorrect. In this paper a cardinality extension principle is proposed. It realizes arithmetic operations on probabilistic FSofN. This principle was proposed in 2 versions: as a normal and as a generalized version that enables context-dependent constraints to be taken into account. In addition a new form of the possibilistic, constraint-extension principle of Klirr [7], [8] is presented. In this paper many examples are given that illustrate computations with both extension principles to make them less abstract and more user-friendly.
Mathematics, 2021
A formal model of an imprecise number can be given as, inter alia, a fuzzy number or oriented fuzzy numbers. Are they formally equivalent models? Our main goal is to seek formal differences between fuzzy numbers and oriented fuzzy numbers. For this purpose, we examine algebraic structures composed of numerical spaces equipped with addition, dot multiplication, and subtraction determined in a usual way. We show that these structures are not isomorphic. It proves that oriented fuzzy numbers and fuzzy numbers are not equivalent models of an imprecise number. This is the first original study of a problem of a dissimilarity between oriented fuzzy numbers and fuzzy numbers. Therefore, any theorems on fuzzy numbers cannot automatically be extended to the case of oriented fuzzy numbers. In the second part of the article, we study the purposefulness of a replacement of fuzzy numbers by oriented fuzzy numbers. We show that for a portfolio analysis, oriented fuzzy numbers are more useful than ...
Information Sciences, 1991
Extended fuzzy numbers, previously called fuzzy intervals, are discussed by using the resolution identity and the extension principle. The regularity and the spread are defined for describing the algebraic properties of extended fuzzy numbers. Arithmetic operations on a-level set intervals are suggested instead of general set operations in order to reduce the amount of computation. A sufficient and necessary condition for solving A + X= C is derived. Tbe exact solution for A + X = C is obtained. Finally, A -A = 0 (a fuzzification of the crisp O), which is a natural extension from the nonfuzzy field, is proved.
International Journal of Industrial Mathematics, 2019
In this paper, we have studied the basic arithmetic operations for developed parabolic fuzzy numbers by using the concept of the transmission average, which was already implied in [F. Abbasi et al., A new attitude coupled with fuzzy thinking to fuzzy rings and fields, Journal of Intelligent and Fuzzy Systems, 2015] in its rudimentary form and was finally presented in its fully-fledged form in [F. Abbasi et al., A new and efficient method for elementary fuzzy arithmetic operations on pseudo-geometric fuzzy numbers, Journal of Fuzzy Set Valued Analysis, 2016]. The major advantage of these operations is that they findings are closer to reality than extension principle-based fuzzy arithmetic operations (in the domain of the membership function) or interval arithmetic (in the domain of $alpha$-cuts). A technical example is given to illustrate applying the method. The proposed method can model and analyze the fuzzy system reliability in a more flexible and intelligent ...
ArXiv, 2017
At the first, we revise the Kosinski definition of the sum of ordered fuzzy numbers. The associativity of revised sum is investigated here. In addition, we show that the multiple revised sum of finite sequence of trapezoidal ordered fuzzy numbers depends on its summands ordering.
International Journal of Approximate Reasoning, 1987
This book provides an introduction to fuzzy numbers and the operations using them. The basic definitions and operations are clearly presented with many examples. However, despite the title, applications are not covered. A fuzzy number is defined as a fuzzy subset of the reals that is both normal and convex; fuzzy numbers may also be defined over other sets of numbers, including the integers. Fuzzy arithmetic may be regarded as a fuzzy generalization of interval arithmetic, which has been extensively studied. However, the connections between fuzzy arithmetic and interval arithmetic are not acknowledged here. Because a number of the results for fuzzy arithmetic duplicate those previously obtained for interval arithmetic, this is inappropriate. Intervals of confidence are used in Chapter 1 to introduce fuzzy numbers. The extension of basic arithmetic operations to fuzzy numbers is presented. Several restricted sets of fuzzy numbers are defined; these include L-R fuzzy numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers. A fuzzy number may be combined with a random variable to form a hybrid number. Operations using such hybrid numbers are covered in Chapter 2. Also covered in this chapter are sheaves, or samples, of fuzzy numbers and a measure of dissimilarity between fuzzy numbers referred to as a dissemblance index. Additional classes of fuzzy numbers are described: multidimensional fuzzy numbers and fuzzy numbers whose defining membership functions are either fuzzy or random. Fuzzy versions of modular arithmetic and complex numbers are presented in Chapter 3. Sequences and series of fuzzy numbers are discussed, and fuzzy factorials are defined. Properties of functions of fuzzy numbers are presented, with emphasis on exponential, trigonometric, and hyperbolic functions; derivatives are also mentioned. Several ways to describe and compare fuzzy numbers are covered in Chapter 4. These include deviations, divergences, mean intervals of confidence, agreement indices, and upper and lower bounds. However, the general problems
isara solutions, 2018
Fuzzy arithmetic is based on properties of fuzzy numbers. Each fuzzy number can uniquely be determined by its α - cut and the α - cut of each fuzzy number (0 α 1) is a closed interval of real numbers. In this paper, fuzzy arithmetic +, –, . and ÷ have been developed by trapezoidal fuzzy numbers.
Information Sciences, 1988
A precise formulation of Zadeh's extension principle for fuzzy number theory is given. Moreover, it is proved that if an equational property p = q holds for the real numbers and the variables occurring in the expressions p and 4 are distinct, then this property holds for the fuzzy numbers as well.
2007
We analyze a decomposition of the fuzzy numbers (or intervals) which seems to be of interest in the study of some properties of fuzzy arithmetic operations and, in particular, in the analysis of fuzziness, of shape-preservation (symmetry) and distributivity of multiplication and division. By the use of the same decomposition, we suggest an approximation of multiplication and division to reduce
Soft Computing, 2018
In engineering and social science fields such as sociology and psychology while treating the uncertainties of triangular and trapezoidal fuzzy numbers are not applicable and fuzzy numbers with more parameters and clear definitions of their arithmetic operations are needed. In order to fill this gap in the literature, in this paper we propose the new fuzzy arithmetic operations based on transmission average on pseudo-octagonal fuzzy numbers, which was already implied in Abbsi et al. (J Intell Fuzzy Syst 29:851-861, 2015) in its rudimentary form and was finally presented in its fully fledged form in Abbsi et al. (J Fuzzy Set Valued Anal 2:1-18, 2016). The properties of these propose operations and their fundamental qualities are discussed. Several illustrative examples were given to show the accomplishment and ability of the proposed method. We present a new method for fuzzy system reliability analysis based on the fuzzy arithmetic operations of transmission average, where the reliabilities of the components of a system are represented by pseudo-octagonal fuzzy numbers defined in the universe of discourse [0, 1]. The proposed method has the advantages of modeling and analyzing fuzzy system reliability in a more flexible and more intelligent manner. Finally, dissatisfaction of a vice training and further education of arbitrary university with two faculties is considered in fuzzy environment.
Applied Soft Computing, 2015
In this paper, a new approach for defuzzification of generalized fuzzy numbers is established. This method uses the incentre point of a triangle where the three bisector lines of its angles meet. Coordinates of incentre point can also be easily calculated by the "Mathematica" package to solve problems of defuzzification and ranking fuzzy numbers. Some numerical examples are illustrated to show the utility of proposed method.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.