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2012, Communications in Computer and Information Science
The fuzzy transform setting (F-transform) is proposed as a tool for representation and approximation of type-1 and type-2 fuzzy numbers; the inverse F-transform on appropriate fuzzy partition of the membership interval [0,1] is used to characterize spaces of fuzzy numbers in such a way that arithmetic operations are de…ned and expressed in terms of the F-transform of the results. A type-2 fuzzy number is represented as a particular fuzzy-valued function and it is expressed in terms of a two-dimensional F-transform where the …rst dimension represents the universe domain and the second dimension represents the membership domain. Operators on two dimensional F-transform are then proposed to approximate arithmetic operations with type 2 fuzzy numbers.
2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013
AbstractóThe fuzzy transform setting (F-transform) is proposed as a tool for representation and approximation of type-2 fuzzy numbers; a type-2 fuzzy number is represented as a particular fuzzy-valued function and it is expressed in terms of a twodimensional F-transform where the rst dimension represents the universe domain and the second dimension represents the membership domain. Operators on two dimensional F-transform are then proposed to approximate arithmetic operations with type-2 fuzzy numbers.
Fuzzy Sets and Systems, 2016
In this paper we will prove that in most of the cases the extended inverse fuzzy transform preserves the quasi-concavity of a fuzzy number and hence it can be used to generate fuzzy numbers by approximating the restriction of the membership function to its support. In the case of continuous fuzzy numbers with cores containing more than one element, the rate of uniform convergence is of linear type and the same holds when we approximate the important characteristics of a fuzzy number such as the value or the ambiguity. Moreover we have the preservation of the support and the convergence of the core which in addition can be determined precisely. In the case of continuous fuzzy numbers with one-element core, it is in general necessary to normalize the approximation, but the support is preserved again and the core can be determined exactly in this case too. Moreover, the approximations have again linear rate of uniform convergence.
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.
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.
Springer eBooks, 2019
In this chapter, preliminaries related to fuzzy numbers have been discussed. Fuzzy numbers and fuzzy arithmetic may be considered as an extension of classical real numbers and its arithmetic. As such, we may understand fuzzy arithmetic as basics for handling fuzzy eigenvalue problems, nonlinear equations, system of nonlinear equations (Abbasbandy and Asady 2004), differential equations (Chakraverty et al. 2016), etc. There exist different types of fuzzy numbers as discussed in Hanss (2005), but for the sake of completeness of the chapter, triangular, trapezoidal, and Gaussian fuzzy numbers based on the membership functions have only been included here. Further, the conversions of these fuzzy numbers to fuzzy intervals with respect to the concept of intervals (Chap. 1) are incorporated. In this regard, the interval arithmetic mentioned in Chap. 1 has been further extended to fuzzy intervals in Sect. 3.4. 3.1 Preliminaries of Fuzzy Numbers A convex fuzzy setà is a fuzzy set having membership function μÃ(x), satisfying μÃ(λx 1 + (1 − λ)x 2) ≥ min(μÃ(x 1), μÃ(x 2)), (3.1) where x 1 , x 2 ∈ X and λ ∈ [0, 1]. Figure 3.1 depicts convex and non-convex fuzzy sets. Convex fuzzy sets defined with respect to universal set (set of all real numbers) may be interpreted as fuzzy numbers. In this respect, the classical definition of fuzzy number is given below. Fuzzy number: A fuzzy setà is referred as a fuzzy numberã if the following properties are satisfied:
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.
Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology, 2013
In this paper we present a general framework to face the problem of evaluate fuzzy quantities. A fuzzy quantity is a fuzzy set that may be non normal and/or non convex. This new formulation contains as particular cases the ones proposed by Fortemps and Roubens [7], Yager and Filev [12, 13] and follows a completely different approach. It starts with idea of "interval approximation of a fuzzy number" proposed, e.g., in [4, 8, 9].
Journal of Intelligent and Fuzzy Systems, 2017
In this paper, we continue a study of approximation properties of the fuzzy transform (F-transform) with the partition generated by the Shepard kernel. We make the error estimate in terms of a modulus of continuity which has a higher order than the previously known. Also, we obtain the error estimate of the iterative F-transform-based method for linear Fredholm integral equations of the second kind.
Iranian Journal of Fuzzy Systems, 2009
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.
Fuzzy Sets and Systems, 1998
Fuzzy numbers, and more generally linguistic values, are approximate assessments, given by experts and accepted by decision-makers when obtaining more accurate values is impossible or unnecessary. To simplify the task of representing and handling fuzzy numbers, several authors have introduced real indices in order to capture the information contained in a fuzzy number. In this paper we propose two parameters, value and ambiguity, for this purpose. We use these parameters to obtain canonical representations and to deal with fuzzy numbers in decision-making problems. Several examples illustrate these ideas.
Advanced Studies in Contemporary Mathematics, 2011
In this paper, we have suggested a new trapezoidal approximation of a fuzzy number, preserving the core and the expected value of fuzzy numbers. We have proved that the trapezoidal approximation of fuzzy numbers preserving the core and the expected value is always a fuzzy number. We have discussed the properties of this approximation.
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
Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), 2011
Fuzzy number approximation by trapezoidal fuzzy numbers which preserves the expected interval is discussed. New operators that fulfill additional requirements for the core and support of the fuzzy number are suggested. These supplementary conditions guarantee the proper interpretation of the solution even for very skew original fuzzy numbers.
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.
Journal of Nonlinear Analysis and Application, 2013
In this paper, several new algebraic mathematics for positive fuzzy numbers of type (a, a, a, a) are devised and do not need the computation of α-cut of the fuzzy number. Direct mathematical expressions to evaluate exponential, square root, logarithms, inverse exponential etc. of positive fuzzy numbers of type (a, a, a, a) are obtained using the basic analytical principles of algebraic mathematics and Taylor series expansion. At the end, Various numerical examples are also solved to demonstrate the use of contrived expressions.
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 ...
Lecture Notes in Computer Science, 2005
Algebra of ordered fuzzy numbers (OFN) is defined to handle with fuzzy inputs in a quantitative way, exactly in the same way as with real numbers. Additional two structures: algebraic and normed (topological) are introduced to define a general form of defuzzyfication operators. A useful implementation of a Fuzzy Calculator allows counting with the general type membership relations.
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.
Journal of Mathematical Analysis and Applications, 2000
2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015
Recently, plasmons in graphene have been observed experimentally using scattering scanning near-field optical microscopy. In this paper, we develop a simplified analytical approach to describe the behavior in triangular samples. Replacing Coulomb interaction by a short-range one reduces the problem to a Helmholtz equation, amenable to analytical treatment. We demonstrate that even with our simplifications, the system still exhibits the key features seen in the experiment.
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