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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 | 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).

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F-Compact operator1_p1-2

  • 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).