$mathcal{I}$-convergence in Fuzzy Cone Normed Spaces

An ideal I is a family of subsets of positive integers $\mathbb{N}$ which is closed under taking finite unions and subsets of its elements. In [8], Kostyrko et al., introduced the concept of ideal convergence as a sequence (x k ) of real numbers is said to be I-convergent to a real number $\ell$, if for each ϵ > 0 the set $\{k\in\mathbb{N}:|x_{k}-\ell|\geq\varepsilon\}$ belongs to I. The aim of this paper is to introduce and study the notion of λ-ideal convergence in intuitionistic fuzzy 2-normed space as a variant of the notion of ideal convergence. Also I λ -limit points and I λ -cluster points have been defined and the relation between them has been establish. Furthermore, Cauchy and I λ -Cauchy sequences are introduced and studied.

In this paper we introduce the concept of statistical convergence of a sequence in fuzzy n-normed space and defined limit point, statistical limit point and statistical cluster point of a sequence in fuzzy n-normed space. Also we establish the relationship between limit point, statistical limit point and statistical cluster point of a sequence in fuzzy n-normed space.

The aim of present work is to introduce and study lacunary statistical limit and lacunary statistical cluster points for generalized difference sequences of fuzzy numbers. Some inclusion relations among the sets of ordinary limit points, statistical limit points, statistical cluster points, lacunary statistical limit points, and lacunary statistical cluster points for these type of sequences are obtained.

This paper is concerned with the notions of statistical limit and cluster points defined by Fridy. Following the concept of a Δ-density for a subset of a time scale, we established a generalization of these notions which are called Δ-limit and Δ-cluster points for a function defined on a time scale $\mathbb{T}$ .

Lacunary ideal convergence in intuitionistic fuzzy normed linear spaces

On the ideal convergence of subsequences and rearrangements of a real sequence

On Statistical Limit Points and the Consistency of Statistical Convergence

An ideal I is a family of subsets of positive integers N which is closed under taking finite unions and subsets of its elements. In [19], Kostyrko et al. introduced the concept of ideal convergence as a sequence (xk) of real numbers is said to be I-convergent to a real number e, if for each ? > 0 the set {k ? N : |xk - e| ? ?} belongs to I. The aim of this paper is to introduce and study the notion of ?-ideal convergence in intuitionistic fuzzy normed spaces as a variant of the notion of ideal convergence. Also I? -limit points and I?-cluster points have been defined and the relation between them has been established. Furthermore, Cauchy and I?-Cauchy sequences are introduced and studied. .

The main aim of this paper is to introduce I-st limit points and I-st cluster points of a sequence of fuzzy numbers and also study some of its basic properties. Conditions for a I-st limit point of a I-st cluster point are investigated.

In the current work we introduce the notions of levelwise statistical limit point and levelwise statistical cluster point of a sequence of fuzzy numbers and discuss the relations between the sets of ordinary limit points, levelwise limit points, levelwise statistical limit points, statistical cluster points and levelwise statistical cluster points of a sequence of fuzzy numbers. Finally, we show that the Bolzano–Weierstrass Theorem is not valid for levelwise statistical convergence.

We show that an ideal mathcal {I} on the positive integers is meager if and only if there exists a bounded nonconvergent real sequence x such that the set of subsequences [resp. permutations] of x which preserve the set of mathcal {I}-limit points is comeager and, in addition, every accumulation point of x is also an mathcal {I}-limit point (that is, a limit of a subsequence (x_{n_k}) such that {n_1,n_2,ldots ,} notin mathcal {I}). The analogous characterization holds also for mathcal {I}-cluster points.

On the relationship between ideal cluster points and ideal limit points

In this study, we define a new type of statistical limit point using the notions of statistical convergence with respect to the $J_p$ power series method and then we present some examples to show the relations between these points and ordinary limit points. After that we also study statistical limit points of a sequence with the help of a modulus function in the sense of the $J_p$ power series method. Namely, we define $f-J_p$-statistical limit and cluster points of the real sequences and compare the set of these limit points with the set of ordinary points.

λ-Statistical limit points of the sequences of fuzzy numbers

This article discusses a variety of important notions, including ideal convergence and ideal Cauchyness of topological sequences produced by fuzzy normed spaces. Furthermore, the connections between the concepts of the ideal limit and ideal cluster points of a sequence in a fuzzy normed linear space are investigated. In a fuzzy normed space, we investigated additional effects, such as describing compactness in terms of ideal cluster points and other relevant but previously unresearched ideal convergence and adjoint ideal convergence aspects of sequences and nets. The countable compactness of a fuzzy normed space and its link to it were also defined. The terms ideal and its adjoint divergent sequences are then introduced, and specific aspects of them are explored in a fuzzy normed space. Our study supports the importance of condition (AP) in examining summability via ideals. It is suggested to use a fuzzy point symmetry-based genetic clustering method to automatically count the number of clusters in a data set and determine how well the data are fuzzy partitioned. As long as the clusters have the attribute of symmetry, they can be any size, form, or convexity. One of the crucial ways that symmetry is used in fuzzy systems is in the solution of the linear Fuzzy Fredholm Integral Equation (FFIE), which has symmetric triangular (Fuzzy Interval) output and any fuzzy function input.