Barnabás Bede

First order linear fuzzy differential equations under generalized differentiability

First order linear fuzzy differential equations are investigated. We interpret a fuzzy differential equation by using the strongly generalized differentiability concept, because under this interpretation, we may obtain solutions which have a decreasing length of their support (which means a decreasing uncertainty). In several applications the behaviour of these solutions better reflects the behaviour of some real-world systems. Derivatives of the H-difference and the product of two functions are obtained and we provide solutions of first order linear fuzzy differential equations, using different versions of the variation of constants formula. Some examples show the rich behaviour of the solutions obtained.

Generalized differentiability of fuzzy-valued functions

In the present paper, using novel generalizations of the Hukuhara difference for fuzzy sets, we introduce and study new generalized differentiability concepts for fuzzy valued functions. Several properties of the new concepts are investigated and they are compared to similar fuzzy differentiabilities finding connections between them. Characterization and relatively simple expressions are provided for the new derivatives.

Almost periodic fuzzy-number-valued functions

In this paper we develop a theory of almost periodic fuzzy functions, i.e. of the almost periodic functions of real variable and with values fuzzy real numbers, Although the class of fuzzy real numbers does not form a linear normed space, the majority of main properties of almost periodic functions with values in Banach spaces are extended to this case. Applications to fuzzy differential equations and to (fuzzy) dynamical systems are given.

Towards a higher degree F-transform

The aim of this study is to show how the F-transform technique can be generalized from the case of constant components to the case of polynomial components. After a general presentation of an F^m- transform, m>=0, a detailed characterization of the F^1- transform is given. We apply a numeric integration technique in order to simplify the computation of F^1- transform components. The inverse F^m- transform, m>0, is defined similarly to the ordinary inverse F-transform. The quality of approximation using the inverse F^m- transform increases with an increase in m.

Quadrature rules for integrals of fuzzy-number-valued functions

In this paper, we introduce some quadrature rules for the Henstock integral of fuzzy-number-valued mappings by giving error bounds for mappings of bounded variation and of Lipschitz type. We also consider generalizations of classical quadrature rules, such as midpoint-type, trapezoidal and three-point-type quadrature. Finally, we study δ-fine quadrature rules and we present some numerical applications.

On various eigen fuzzy sets and their application to image reconstruction

In this study, we formulate and solve a problem of image reconstruction using eigen fuzzy sets. Treating images as fuzzy relations, we propose two algorithms of generating eigen fuzzy sets that are used in the reconstruction process. The first one corresponds to a convex combination of eigen fuzzy set equations, i.e., fuzzy relational equations involving convex combination of max-min and min-max compositions. In the case of the first algorithm, various eigen fuzzy sets can be generated by changing the parameter controlling the convex combination of the corresponding equations. The second algorithm generates various eigen fuzzy sets with respect to the original fuzzy relation using a permutation matrix. A thorough comparison of the proposed algorithms and a conventional algorithm which reconstructs an image using the greatest and smallest eigen fuzzy sets is presented as well. In the experiments, 10,000 artificial images of size 5x5 pixels. The approximation error in the case of the first/second algorithm is decreased to 68.2%/97.9% of that of the conventional algorithm, respectively. Furthermore, through the experimentation using real images extracted from Standard Image DataBAse (SIDBA), it is confirmed that the approximation error of the first algorithm is decreased to 41.5% of that of the conventional one.

Approximation properties of fuzzy transforms

We enlarge the class of fuzzy transforms (F-transforms) by considering different types of fuzzy partitions. New types of F-transforms are constructed based on B-splines, Shepard kernels, Bernstein basis polynomials and Favard-Szasz-Mirakjan type operators. We study approximation properties of F-transforms. Particular cases of the classical Korovkins theorems are presented, together with new error estimates for different types of fuzzy transforms. It turns out that F-transforms can be studied in the framework of classical approximation theory.

Approximation and Shape Preserving Properties of the Bernstein Operator of Max-Product Kind

Starting from the study of the Shepard nonlinear operator of max-prod type by Bede et al. (2006, 2008), in the book by Gal (2008), Open Problem 5.5.4, pages 324–326, the Bernstein max-prod-type operator is introduced and the question of the approximation order by this operator is raised. In recent paper, Bede and Gal by using a very complicated method to this open question an answer is given by obtaining an upper estimate of the approximation error of the form 𝐶𝜔1√(𝑓;1/𝑛) (with an unexplicit absolute constant 𝐶g0) and the question of improving the order of approximation 𝜔1√(𝑓;1/𝑛) is raised. The first aim of this note is to obtain this order of approximation but by a simpler method, which in addition presents, at least, two advantages: it produces an explicit constant in front of 𝜔1√(𝑓;1/𝑛) and it can easily be extended to other max-prod operators of Bernstein type. However, for subclasses of functions 𝑓 including, for example, that of concave functions, we find the order of approximation 𝜔1(𝑓;1/𝑛), which for many functions 𝑓 is essentially better than the order of approximation obtained by the linear Bernstein operators. Finally, some shape-preserving properties are obtained.

Approximation by pseudo-linear operators

The approximation operators provided by classical approximation theory use exclusively as underlying algebraic structure the linear structure of the reals. Also they are all linear operators. We address in the present paper the following problems: Need all the approximation operators be linear? Is the linear structure the only one which allows us to construct particular approximation operators? As an answer to this problem we propose new, particular, pseudo-linear approximation operators, which are defined in some ordered semirings. We study these approximations from a theoretical point of view and we obtain that these operators have very similar properties to those provided by classical approximation theory. In this sense we obtain uniform approximation theorems of Weierstrass type, and Jackson-type error estimates in approximation by these operators.

Fuzzy Differential Equations in Various Approaches

1. Introduction. -2. Basic Concepts. -3. Fuzzy Calculus. -4. Fuzzy Differential Equations. -Mathematical Background. -Index.