This document discusses F-compact operators on probabilistic Hilbert spaces. It begins with an introduction that defines probabilistic metric spaces and probabilistic Hilbert spaces. It then provides preliminary definitions, including the definition of a distribution function and triangle norms. The document will define F-compact operators on probabilistic Hilbert spaces and examine some of their main properties.

1. 1 | P a g e
𝑭-Compact operator on probabilistic Hilbert space
Assist. Prof. Dr. Radhi I. M. Ali,
Rana Aziz Yousif Al-Muttalibi
University of Baghdad
College of Science for Women
Department of Mathematics
radi52@yahoo.com
rana.yousif1982@gmail.com
Abstract. This paper deals with the 𝐹-compact operator defined on probabilistic
Hilbert space and gives some of its main properties.
Keywords. Bounded set, 𝐹-compact operators, 𝜏-convergent sequences, and
probabilistic Hilbert space.
1. Introduction
The notion of probabilistic metric space was introduced by Menger [3]. It was
represented as a generalization of the notion of metric space. Menger replaced the
distance between the two points 𝑝 and 𝑞 by a distribution function 𝐹𝑝𝑞(𝑥) for real
number 𝑥 means that the distance between 𝑝 and 𝑞 is less than 𝑥. The notion of
probabilistic metric space corresponds to the situations when we do not know exactly
the distance between two points and we know only the possible probabilistic values of
these distances. An important family of probabilistic spaces are probabilistic inner
product spaces which were introduced by C. Sklar and B. Schweizer in 1983 [5]. The
definition of probabilistic Hilbert space was also introduced in 2007 by Su, Y.; Wang,
X.; and Gao, J. [7]. In the sequel, we shall define the compact operator on probabilistic
Hilbert space and examine some important theorems about it.

2. 2 | P a g e
2. Preliminaries
Definition (2.1) [2] (Distribution function)
A distribution function (d.f.) is a function 𝐹 defined on the extended real line
𝑅̅: [−∞, +∞] that is non-decreasing and left-continuous, such that 𝐹(−∞) = 0 and
𝐹(+∞) = 1. The set of all d.f. will be denoted by ∆; the subset of ∆ is formed by the
proper d.f.s, i.e. by those d.f.s 𝐹 for which
lim
𝑡→−∞
𝐹(𝑡) = 0 and lim
𝑡→+∞
𝐹(𝑡) = 1
will be denoted by 𝐷.
A special element of ∆ is the function which is defined by
𝜀0(𝑡) = {
0, 𝑡 ≤ 0
1, 𝑡 > 0
and denoted by 𝐻(𝑡).
Moreover, a distance distribution function (briefly, a d .d .f.) is a non-decreasing
function 𝐹 defined on 𝑅+
that satisfies 𝐹(0) = 0 , 𝐹(∞) = 1 and is left continuous on
(0, ∞). The set of all d.d.f's will be denoted by ∆+
; and the set of all 𝐹 in ∆+
for which
𝑙−
𝐹(∞) = 𝐹(∞) = 1 will be denoted by 𝒟+
.
Definition (2.2) [5]
A triangle norm (t-norm) is an associative binary operation on 𝐼 = [0,1] i.e.
𝑇: [0,1] × [0,1] ⟶ [0,1] is called t-norm if and only if for all 𝑎, 𝑏, 𝑐, and 𝑑 ∈ [0,1] the
following hold.
1. 𝑇(𝑎, 1) = 𝑎;
2. 𝑇(𝑎, 𝑏) = 𝑇(𝑏, 𝑎);
3. 𝑇(𝑎, 𝑏) ≤ 𝑇(𝑐, 𝑑), ∀𝑎 ≤ 𝑐, 𝑏 ≤ 𝑑;
4. 𝑇(𝑎, 𝑇(𝑏, 𝑐)) = 𝑇(𝑇(𝑎, 𝑏), 𝑐).
The most important t- norms are the functions which are defined as follows.
1. 𝑊(𝑎, 𝑏) = 𝑚𝑎𝑥(𝑎 + 𝑏 − 1,0),
2. ∏(𝑎, 𝑏) = 𝑎𝑏,
3. 𝑀(𝑎, 𝑏) = 𝑚𝑖𝑛(𝑎, 𝑏).