It is shown that every fuzzy n-normed space naturally induces a locally convex topology, and that every finite dimensional fuzzy n-normed space is complete.

The similarity measure of fuzzy numbers is very important in many research fields such as pattern recognition [[5],[6]] and risk analysis in fuzzy environment [[1],[3],[15]]. Some methods have been presented to calculate the degree of similarity between fuzzy numbers [[1][4],[8],[15]]. In [16], Wen presented A modified similarity measure of generalized fuzzy numbers. Pandey et al.,[7] proposed ...

we have the crisp vector → PQ= (y(1)−x(1),y(2)−x(2), . . . ,y(n)−x(n)) in a pseudo-fuzzy vector space Fn p (1)= {(a(1),a(2), . . . ,a(n))1∀(a(1),a(2), . . . ,a(n))∈Rn}. There is a one-to-one onto mapping P = (x(1),x(2), . . . ,x(n)) ↔ P̃ = (x(1),x(2), . . . , x)1. Therefore, for the crisp vector → PQ, we can define the fuzzy vector → P̃ Q̃= (y(1)− x(1),y(2)−x(2), . . . ,y(n)−x(n))1 = Q̃ P̃ . Let the...

we have the crisp vector → PQ= (y(1)−x(1),y(2)−x(2), . . . ,y(n)−x(n)) in a pseudo-fuzzy vector space Fn p (1)= {(a(1),a(2), . . . ,a(n))1∀(a(1),a(2), . . . ,a(n))∈Rn}. There is a one-to-one onto mapping P = (x(1),x(2), . . . ,x(n)) ↔ P̃ = (x(1),x(2), . . . , x)1. Therefore, for the crisp vector → PQ, we can define the fuzzy vector → P̃ Q̃= (y(1)− x(1),y(2)−x(2), . . . ,y(n)−x(n))1 = Q̃ P̃ . Let the...

This paper extends the author’s earlier work on the Law of Large Numbers for fuzzy numbers [2] to the case where the fuzzy numbers are of type L-R. Namely, we shall define a class of Archimedean triangular norms in which the equality lim n→∞ Nes(mn − ≤ ηn ≤ mn + = 1, for any > 0, holds for all sequences of fuzzy numbers, ξi = (Mi, α, β)LR, i ∈ N, with twice differentiable and concave shape func...

Properties valid on the classical theory (Boolean laws) have been extended to fuzzy set theory and so-called Boolean-like laws. The fact that they are not always valid in any standard fuzzy set theory induced a wide investigation. In this paper we show the sufficient and necessary conditions that the Boolean-like law y ≤ I(x, y) holds in fuzzy logic. We focus the investigation on the following ...

This paper presents a mathematical model for a flow shop scheduling problem consisting of m machine and n jobs with fuzzy processing times that can be estimated as independent stochastic or fuzzy numbers. In the traditional flow shop scheduling problem, the typical objective is to minimize the makespan). However,, two significant criteria for each schedule in stochastic models are: expectable m...

In this paper we introduce the root of a fuzzy number, and we present aniterative method to nd it, numerically. We present an algorithm to generatea sequence that can be converged to n-th root of a fuzzy number.

The primary purpose of this paper is to introduce the notion of fuzzy n-normed linear space as a generalization of n-normed space. Ascending family of α-n-norms corresponding to fuzzy n-norm is introduced. Best approximation sets in α-n-norms are defined. We also provide some results on best approximation sets in α-n-normed space.