SlideShare uma empresa Scribd logo
1 de 3
Baixar para ler offline
Fuzzifying Topological System
1
2.preliminaries
In this section, we introduce some abstraction and results in fuzzifying topology, which
will be used in the outcome. For the details, we present [7,23].First we describe a verity of fuzzy logical
symbols and properties. For any formulas𝜑, the symbol [𝜑] means the truth value of 𝜑, where the set of
truth values in the unit interval [0,1]. A formula is valid, we write ⊨ 𝜑 if and only if [𝜑]=1 for every
interpretation. Let x be a universe of discourse, and let P(X) and 𝓅(𝑋) denote the classes of all crisp
subsets and fuzzy subsets of X, respectively, Then, for 𝐴
̃,𝐵
̃ 𝜖 𝓅(𝑋), and any x𝜖X,
(1) [¬𝛼]=1-[𝛼];
[𝛼 ∧ 𝛽] = min ([𝛼], [𝛽]);
[𝛼 → 𝛽] = min (1,1 − [𝛼] + [𝛽]);
[∀𝑥 ∝ (𝑥)] = 𝑖𝑛𝑓𝑥𝜖𝑋 [∝ (𝑥)]
[𝑥 ∈ 𝐴
̃] = 𝐴
̃(𝑥)[∃𝑥 ∝ (𝑥)] ∶= [¬(∀𝑥¬∝ (𝑥))]
(2) [𝛼 ∨ 𝛽] ∶= [¬(¬𝛼 ∧ ¬𝛽)];
[𝛼 ↔ 𝛽] ∶= [(𝛼 → 𝛽) ∧ (𝛽 → 𝛼)];
[𝐴
̃ ⊆ 𝐵
̃] ∶= [∀𝑥(𝑥 ∈ 𝐴
̃ → 𝑥 ∈ 𝐵
̃)] 0
= 𝑖𝑛𝑓𝑥∈𝑋 min (1,1 − 𝐴
̃(𝑥) + 𝐵
̃(𝑥));
[𝐴
̃ ≡ 𝐵
̃] ∶= [(𝐴
̃ ⊆ 𝐵
̃)⋀(𝐵
̃ ⊆ 𝐴
̃)];
[𝛼 ⩒ 𝛽] ∶= [¬(𝛼 → ¬𝛽)] = min (1, (𝛼) + (𝛽));
[𝛼 ⩑ 𝛽] ∶= [¬𝛼 → 𝛽)] = 𝑚𝑎𝑥(0, [𝛼] + [𝛽] − 1).
Second, we give the following definitions and results in fuzzifying topology which are useful in the rest of the
present paper.
Definition 1. (cf. [𝟕])
Let X be a set. If 𝓅𝜖𝓅(𝑃(𝑋)) satisfies the following conditions:
(1) 𝓅(X) = 𝓅(ϕ) = 1;
(2) For any A, B,𝓅(𝐴 ∩ 𝐵) ≥ 𝓅(𝐴) ∧ 𝓅(𝐵);
(3) For any {𝐴𝜆: 𝜆 𝜖𝐴},𝓅(⋃ 𝐴𝜆
𝜆∈𝐴
) ≥ ⋀𝜆𝜖𝐴𝓅(𝐴𝜆).
Then 𝓅 is called a fuzzifying topology, (X, 𝓅)is called a fuzzifying topological space, A𝜖𝑃(𝑋) with 𝓅(𝐴) > 0 is
called a fuzzifying open set (write as 𝐴𝜖
𝓅), and 𝓅(𝐴) is called the degree of open of A.
Definition 2. (cf.[𝟕, 𝟐𝟑])
Let (X, 𝓅) be a fuzzifying topological space. Then
(1) 𝓅𝜖𝓅(𝑃(𝑋)), defined by 𝓅𝑐(𝐴) = 𝓅(𝑋~𝐴)(∀𝐴 ∈ 𝑃(𝑋)), is called a fuzzifying cotopology, B∈ 𝑃(𝑋) with
𝓅(𝑋~𝐴) > 0 is called a fuzzifying closed set (write as 𝐵𝜖
𝓅𝑐
), and 𝓅𝑐
(𝐴) is called the degree of close of
A, where X~A is the complement of A.
(2) 𝒩
𝑥 ∈ 𝓅(𝑃(𝑋)) defined by
𝒩
𝑥(A)= ⋁ 𝓅(𝐵)
𝑥𝜖𝐵⊆𝐴 (∀𝐴𝜖𝑃(𝑋))
is called the fuzzifying neighborhood system of a point x∈X, C∈ p(X) with𝒩𝑥(C)> 0 is called a fuzzifying
neighborhood of x (write as 𝐶∈
𝒩
𝑥), and 𝒩
𝑥(𝐶) is called the degree of C being a neighborhood of x.
(3) The fuzzifying cluster operator Cl(A)𝜖 𝓅(𝑋) of a set A⊆X is defined by Cl(A)(x) = 1-𝒩
𝑥(X~A) (∀𝑥 ∈ 𝑋),
and Cl(A)(x) is called the degree of x being a cluster point of A.
(4) The fuzzifying interior operator Int(A)∈ 𝓅(𝑋) of a set A⊆ 𝑋 is defined by Int(A)(x) = 𝒩
𝑥(𝑋~𝐴)(∀𝑥 ∈ 𝑋),
and Int(A)(x) is called the degree of x being an interior point of A.
Fuzzifying Topological System
2
Definition 3.(𝒄𝒇. [𝟖])
Let (X, 𝓅) be a fuzzifying topological space. Then
(1) A ∈ P(X) satisfying the following condition is called fuzzifying e-open set (write as𝐴𝜖
𝓅𝑒):
𝓅𝑒(𝐴) = ⋀𝜆𝜖𝐴(𝐼𝑛𝑡(𝐶𝑙𝛿(𝐴)))(x)⋁ 𝐶𝑙(𝐼𝑛𝑡𝛿(𝐴))(x))> 0,
Where 𝓅𝑒(A) is called the degree of e-open of A.
(2) A𝜖 𝑃(𝑋) satisfying
𝓅𝑒
𝑐
(A)= 𝓅𝑒(X~A)> 0
is called a fuzzifying e-closed set (write as𝐴𝜖
𝓅𝑒
𝑐
), and 𝓅𝑒
𝑐
(A) is called the degree of e-closed of A.
(3) 𝒩
𝑥
𝑒
∈ 𝓅(𝑃(𝑋)), defined by
𝒩𝑥
𝑒
(𝐴)=⋁𝑥∈𝐵⊆𝐴 𝓅𝑒(B) (∀𝐴 ∈ 𝑃(𝑋))
Is called a fuzzifying e-neighborhood system of a point x𝜖X, C ∈ P(X) with𝒩
𝑥
𝑒
(C)> 0 is called a
fuzzifying e-neighborhood of x (write as𝐶∈
𝒩
𝑥
𝑒
), and 𝒩
𝑥
𝑒
(C) is called the degree of C being an e-
neighborhood of x.
(4) C𝑙𝑒(A) 𝜖, 𝓅(𝑋), defined by
C 𝑙𝑒(A)(x)= 1 − 𝒩
𝑥
𝑒
(X~A) (∀𝑥 ∈ 𝑋),
is called a fuzzifying e-cluster point operator of a subset A⊆X, x∈X with C𝑙𝑒(A)(x)> 0 is called a
fuzzifying e-cluster point of A (write as 𝑥∈
𝐶𝑙𝑒(𝐴)), and C𝑙𝑒(A)(x) is called the degree of x being a
fuzzifying e-cluster point of A.
(5) 𝐼𝑛𝑡𝑒(A)∈ 𝓅(𝑋), defined by
𝐼𝑛𝑡𝑒(A)(x)= 𝒩
𝑥
𝑒
(A) (∀𝑥 ∈ 𝑋),
Is called a fuzzifying e-interior point operator of a subset A⊆X, x∈X with 𝑖𝑛𝑡𝑒(A)(x)> 0 is called a
fuzzifying e-cluster point of A (write as𝑥𝜖
𝐼𝑛𝑡𝑒(𝐴)), and 𝐼𝑛𝑡𝑒(A)(x) is called the degree of x being
fuzzifying e-interior point of A.
Definition 4. (cf.[𝟖])
Let (X, 𝓅) and (Υ, 𝜇) be two fuzzifying topological spaces, 𝑓𝜖Υ𝑥
Satisfying the following condition is called fuzzifying e-continuous function (write as 𝑓𝜖
𝓅):
𝓅∈(𝑓)=⋀𝜇∈𝑝(Υ)min (1,1-𝜇(𝑈) + 𝓅𝑒(𝑓−1
(𝑈)))> 0,
Where 𝓅(𝑓) is called the degree of 𝑓 being an e-continuous function.
Definition 5. (cf. [𝟗])
Let (X,𝓅) and (Υ, 𝜇) be two fuzzifying topological spaces, 𝑓𝜖Υ𝑥
satisfying the
following condition is called fuzzifying e-irresolute function (write as𝑓𝜖
𝓅𝑒):
𝓅𝑒(𝑓) = ⋀𝑢∈𝑝(Υ)min(1,1-𝜇𝑒(U)+ 𝓅𝑒(𝑓−1
(𝑈)) > 0,
where 𝓅𝑒(𝑓) is called the degree of f being an e-irresolute function.
3. Fuzzifying e-𝜃-neighborhood system of a point, fuzzifying e-𝜃closure of a set, fuzzifying e-𝜃-
interior of a set, fuzzifying e-𝜃-open sets, and fuzzifying e-𝜃-closed sets.
Definition 6. 𝒩𝒙
𝒆𝜽
∈ 𝓅(𝑃(𝑋)), defined by
𝒩
𝒙
𝒆𝜽
(A) = ⋁𝒚∈𝒑(𝑿) max (0, 𝒩
𝑥
𝑒
(B)+∧𝑦∈𝐴 𝒩𝑦
𝑒
(X~B)-1),
Fuzzifying Topological System
3
is called a fuzzifying e-neighborhood system of a point x∈ X, C∈ P(X) with 𝒩
𝒙
𝒆𝜽
(C)> 0 is called a
fuzzifying e-neighborhood of x (write as 𝐶∈
𝒩
𝒙
𝒆𝜽
), and 𝒩
𝒙
𝒆𝜽
(C) is called the degree of C being an e-
neighborhood of x.
Theorem 7. The mapping 𝒩𝑒𝜃
: 𝑋 → 𝓅𝑁
(𝑃(𝑋))( the set of all normal fuzzy subsets of P(X)), defined by
𝒩𝑒𝜃
(x)= 𝒩
𝒙
𝒆𝜽
(∀𝑥 ∈ 𝑋), has the following properties:
(1) ⊨ 𝐴∈
𝒩
𝒙
𝒆𝜽
→x∈A (∀𝑥 ∈ 𝑋 , ∀∈ 𝑃(𝑋));
(2) ⊨ A⊆B→ (𝐴∈
𝒩
𝒙
𝒆𝜽
→ 𝐵∈
𝒩
𝒙
𝒆𝜽
)(∀𝑥 ∈ 𝑋, ∀𝐴, 𝐵 ∈ 𝑃(𝑋));
(3) ⊨ (𝐴∈
𝒩𝒙
𝒆𝜽
)⋀( 𝐵∈
𝒩
𝒙
𝒆𝜽
)↔ 𝐴⋂𝐵∈
𝒩
𝒙
𝒆𝜽
(∀𝑥 ∈ 𝑋, ∀𝐴, 𝐵 ∈ 𝑃(𝑋)).
Proof. (1) If[𝐴∈
𝒩
𝒙
𝒆𝜽] = 0, then the result holds. If [𝐴∈
𝒩
𝒙
𝒆𝜽] > 0, then
∨𝐵∈𝑃(𝑋)max (0, 𝒩𝑥
𝑒(𝐵) +∧𝑦∈𝐴 𝒩𝑦
𝑒(𝑋~𝐵) − 1)
=∨𝐵∈𝑃(𝑋)max (0, 𝒩
𝑥
𝑒(𝐵) + [ C 𝑙𝑒(B) ⊆ A] − 1)> 0,
and thus exists a C∈ 𝑃(𝑋) such that 𝒩
𝒙
𝒆
(C)+[C 𝑙𝑒(C) ⊆ A] − 1 > 0. From Theorems 4.2(1) and 5.5(3) in [8],
we have [C 𝑙𝑒(C) ⊆ A] > 1 − 𝒩
𝒙
𝒆
(C)≥ 1 − |𝑥 ∈ 𝐶| ≥ 1 − C 𝑙𝑒(C)(x). Therefore,
[C 𝑙𝑒(C) ⊆ A] = ⋀𝑦∈𝐴(1 − C 𝑙𝑒(C)(y) > 1 − C 𝑙𝑒(C)(x),
and so x∈A. Otherwise, if x∈A, then
⋀𝑦∈𝐴(1 − C 𝑙𝑒(C)(y) > 1 − C 𝑙𝑒(C)(x).

Mais conteúdo relacionado

Mais procurados

8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastavaBIOLOGICAL FORUM
 
Fourier integral of Fourier series
Fourier integral of Fourier seriesFourier integral of Fourier series
Fourier integral of Fourier seriesChintan Mehta
 
Application of partial derivatives with two variables
Application of partial derivatives with two variablesApplication of partial derivatives with two variables
Application of partial derivatives with two variablesSagar Patel
 
t5 graphs of trig functions and inverse trig functions
t5 graphs of trig functions and inverse trig functionst5 graphs of trig functions and inverse trig functions
t5 graphs of trig functions and inverse trig functionsmath260
 
1531 fourier series- integrals and trans
1531 fourier series- integrals and trans1531 fourier series- integrals and trans
1531 fourier series- integrals and transDr Fereidoun Dejahang
 
Уламжлал
УламжлалУламжлал
УламжлалМарт
 
A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...inventionjournals
 
29 conservative fields potential functions
29 conservative fields potential functions29 conservative fields potential functions
29 conservative fields potential functionsmath267
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONDhrupal Patel
 
Integral dalam Bahasa Inggris
Integral dalam Bahasa InggrisIntegral dalam Bahasa Inggris
Integral dalam Bahasa Inggrisimmochacha
 
Numerical Analysis (Solution of Non-Linear Equations)
Numerical Analysis (Solution of Non-Linear Equations)Numerical Analysis (Solution of Non-Linear Equations)
Numerical Analysis (Solution of Non-Linear Equations)Asad Ali
 

Mais procurados (16)

Urysohn's lemma
Urysohn's lemmaUrysohn's lemma
Urysohn's lemma
 
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
 
Fourier integral of Fourier series
Fourier integral of Fourier seriesFourier integral of Fourier series
Fourier integral of Fourier series
 
.Chapter7&8.
.Chapter7&8..Chapter7&8.
.Chapter7&8.
 
Presentation on calculus
Presentation on calculusPresentation on calculus
Presentation on calculus
 
Application of partial derivatives with two variables
Application of partial derivatives with two variablesApplication of partial derivatives with two variables
Application of partial derivatives with two variables
 
t5 graphs of trig functions and inverse trig functions
t5 graphs of trig functions and inverse trig functionst5 graphs of trig functions and inverse trig functions
t5 graphs of trig functions and inverse trig functions
 
Chapter 1
Chapter 1Chapter 1
Chapter 1
 
1531 fourier series- integrals and trans
1531 fourier series- integrals and trans1531 fourier series- integrals and trans
1531 fourier series- integrals and trans
 
Уламжлал
УламжлалУламжлал
Уламжлал
 
A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...A New Approach on the Log - Convex Orderings and Integral inequalities of the...
A New Approach on the Log - Convex Orderings and Integral inequalities of the...
 
29 conservative fields potential functions
29 conservative fields potential functions29 conservative fields potential functions
29 conservative fields potential functions
 
58 slopes of lines
58 slopes of lines58 slopes of lines
58 slopes of lines
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATION
 
Integral dalam Bahasa Inggris
Integral dalam Bahasa InggrisIntegral dalam Bahasa Inggris
Integral dalam Bahasa Inggris
 
Numerical Analysis (Solution of Non-Linear Equations)
Numerical Analysis (Solution of Non-Linear Equations)Numerical Analysis (Solution of Non-Linear Equations)
Numerical Analysis (Solution of Non-Linear Equations)
 

Semelhante a Fuzzy algebra

Some properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spacesSome properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spacesIOSR Journals
 
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix MappingDual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mappinginventionjournals
 
Matrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesMatrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesIOSR Journals
 
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...mathsjournal
 
Fuzzy random variables and Kolomogrov’s important results
Fuzzy random variables and Kolomogrov’s important resultsFuzzy random variables and Kolomogrov’s important results
Fuzzy random variables and Kolomogrov’s important resultsinventionjournals
 
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...IOSR Journals
 
Lecture 1.2 quadratic functions
Lecture 1.2 quadratic functionsLecture 1.2 quadratic functions
Lecture 1.2 quadratic functionsnarayana dash
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...mathsjournal
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...mathsjournal
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...mathsjournal
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...mathsjournal
 
Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...
Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...
Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...mathsjournal
 
Approximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher DimensionsApproximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher DimensionsShrey Verma
 
Homogeneous Components of a CDH Fuzzy Space
Homogeneous Components of a CDH Fuzzy SpaceHomogeneous Components of a CDH Fuzzy Space
Homogeneous Components of a CDH Fuzzy SpaceIJECEIAES
 
On uniformly continuous uniform space
On uniformly continuous uniform spaceOn uniformly continuous uniform space
On uniformly continuous uniform spacetheijes
 
One solution for many linear partial differential equations with terms of equ...
One solution for many linear partial differential equations with terms of equ...One solution for many linear partial differential equations with terms of equ...
One solution for many linear partial differential equations with terms of equ...Lossian Barbosa Bacelar Miranda
 
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric SpaceFixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Spaceinventionjournals
 

Semelhante a Fuzzy algebra (20)

Some properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spacesSome properties of two-fuzzy Nor med spaces
Some properties of two-fuzzy Nor med spaces
 
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix MappingDual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
 
Matrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesMatrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence Spaces
 
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
 
Fuzzy random variables and Kolomogrov’s important results
Fuzzy random variables and Kolomogrov’s important resultsFuzzy random variables and Kolomogrov’s important results
Fuzzy random variables and Kolomogrov’s important results
 
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
Existence Theory for Second Order Nonlinear Functional Random Differential Eq...
 
Lecture 1.2 quadratic functions
Lecture 1.2 quadratic functionsLecture 1.2 quadratic functions
Lecture 1.2 quadratic functions
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
 
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
COMMON FIXED POINT THEOREMS IN COMPATIBLE MAPPINGS OF TYPE (P*) OF GENERALIZE...
 
Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...
Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...
Common Fixed Point Theorems in Compatible Mappings of Type (P*) of Generalize...
 
50120140504018
5012014050401850120140504018
50120140504018
 
Approximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher DimensionsApproximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher Dimensions
 
Homogeneous Components of a CDH Fuzzy Space
Homogeneous Components of a CDH Fuzzy SpaceHomogeneous Components of a CDH Fuzzy Space
Homogeneous Components of a CDH Fuzzy Space
 
5. Rania.pdf
5. Rania.pdf5. Rania.pdf
5. Rania.pdf
 
5. Rania.pdf
5. Rania.pdf5. Rania.pdf
5. Rania.pdf
 
On uniformly continuous uniform space
On uniformly continuous uniform spaceOn uniformly continuous uniform space
On uniformly continuous uniform space
 
One solution for many linear partial differential equations with terms of equ...
One solution for many linear partial differential equations with terms of equ...One solution for many linear partial differential equations with terms of equ...
One solution for many linear partial differential equations with terms of equ...
 
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric SpaceFixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
 

Último

Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17
Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17
Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17Celine George
 
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjStl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjMohammed Sikander
 
Navigating the Misinformation Minefield: The Role of Higher Education in the ...
Navigating the Misinformation Minefield: The Role of Higher Education in the ...Navigating the Misinformation Minefield: The Role of Higher Education in the ...
Navigating the Misinformation Minefield: The Role of Higher Education in the ...Mark Carrigan
 
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING IIII BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING IIagpharmacy11
 
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45MysoreMuleSoftMeetup
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024Borja Sotomayor
 
Features of Video Calls in the Discuss Module in Odoo 17
Features of Video Calls in the Discuss Module in Odoo 17Features of Video Calls in the Discuss Module in Odoo 17
Features of Video Calls in the Discuss Module in Odoo 17Celine George
 
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptxHVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptxKunal10679
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxheathfieldcps1
 
philosophy and it's principles based on the life
philosophy and it's principles based on the lifephilosophy and it's principles based on the life
philosophy and it's principles based on the lifeNitinDeodare
 
MichaelStarkes_UncutGemsProjectSummary.pdf
MichaelStarkes_UncutGemsProjectSummary.pdfMichaelStarkes_UncutGemsProjectSummary.pdf
MichaelStarkes_UncutGemsProjectSummary.pdfmstarkes24
 
ANTI PARKISON DRUGS.pptx
ANTI         PARKISON          DRUGS.pptxANTI         PARKISON          DRUGS.pptx
ANTI PARKISON DRUGS.pptxPoojaSen20
 
Software testing for project report .pdf
Software testing for project report .pdfSoftware testing for project report .pdf
Software testing for project report .pdfKamal Acharya
 
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General QuizPragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General QuizPragya - UEM Kolkata Quiz Club
 
The Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptxThe Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptxNehaChandwani11
 
An Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppAn Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppCeline George
 
An Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptxAn Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptxCeline George
 

Último (20)

Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17
Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17
Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17
 
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjStl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
 
IPL Online Quiz by Pragya; Question Set.
IPL Online Quiz by Pragya; Question Set.IPL Online Quiz by Pragya; Question Set.
IPL Online Quiz by Pragya; Question Set.
 
Navigating the Misinformation Minefield: The Role of Higher Education in the ...
Navigating the Misinformation Minefield: The Role of Higher Education in the ...Navigating the Misinformation Minefield: The Role of Higher Education in the ...
Navigating the Misinformation Minefield: The Role of Higher Education in the ...
 
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING IIII BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
 
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
 
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
Operations Management - Book1.p  - Dr. Abdulfatah A. SalemOperations Management - Book1.p  - Dr. Abdulfatah A. Salem
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024
 
Features of Video Calls in the Discuss Module in Odoo 17
Features of Video Calls in the Discuss Module in Odoo 17Features of Video Calls in the Discuss Module in Odoo 17
Features of Video Calls in the Discuss Module in Odoo 17
 
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptxHVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptx
 
philosophy and it's principles based on the life
philosophy and it's principles based on the lifephilosophy and it's principles based on the life
philosophy and it's principles based on the life
 
MichaelStarkes_UncutGemsProjectSummary.pdf
MichaelStarkes_UncutGemsProjectSummary.pdfMichaelStarkes_UncutGemsProjectSummary.pdf
MichaelStarkes_UncutGemsProjectSummary.pdf
 
ANTI PARKISON DRUGS.pptx
ANTI         PARKISON          DRUGS.pptxANTI         PARKISON          DRUGS.pptx
ANTI PARKISON DRUGS.pptx
 
Software testing for project report .pdf
Software testing for project report .pdfSoftware testing for project report .pdf
Software testing for project report .pdf
 
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General QuizPragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
 
The Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptxThe Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptx
 
Word Stress rules esl .pptx
Word Stress rules esl               .pptxWord Stress rules esl               .pptx
Word Stress rules esl .pptx
 
An Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppAn Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge App
 
An Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptxAn Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptx
 

Fuzzy algebra

  • 1. Fuzzifying Topological System 1 2.preliminaries In this section, we introduce some abstraction and results in fuzzifying topology, which will be used in the outcome. For the details, we present [7,23].First we describe a verity of fuzzy logical symbols and properties. For any formulas𝜑, the symbol [𝜑] means the truth value of 𝜑, where the set of truth values in the unit interval [0,1]. A formula is valid, we write ⊨ 𝜑 if and only if [𝜑]=1 for every interpretation. Let x be a universe of discourse, and let P(X) and 𝓅(𝑋) denote the classes of all crisp subsets and fuzzy subsets of X, respectively, Then, for 𝐴 ̃,𝐵 ̃ 𝜖 𝓅(𝑋), and any x𝜖X, (1) [¬𝛼]=1-[𝛼]; [𝛼 ∧ 𝛽] = min ([𝛼], [𝛽]); [𝛼 → 𝛽] = min (1,1 − [𝛼] + [𝛽]); [∀𝑥 ∝ (𝑥)] = 𝑖𝑛𝑓𝑥𝜖𝑋 [∝ (𝑥)] [𝑥 ∈ 𝐴 ̃] = 𝐴 ̃(𝑥)[∃𝑥 ∝ (𝑥)] ∶= [¬(∀𝑥¬∝ (𝑥))] (2) [𝛼 ∨ 𝛽] ∶= [¬(¬𝛼 ∧ ¬𝛽)]; [𝛼 ↔ 𝛽] ∶= [(𝛼 → 𝛽) ∧ (𝛽 → 𝛼)]; [𝐴 ̃ ⊆ 𝐵 ̃] ∶= [∀𝑥(𝑥 ∈ 𝐴 ̃ → 𝑥 ∈ 𝐵 ̃)] 0 = 𝑖𝑛𝑓𝑥∈𝑋 min (1,1 − 𝐴 ̃(𝑥) + 𝐵 ̃(𝑥)); [𝐴 ̃ ≡ 𝐵 ̃] ∶= [(𝐴 ̃ ⊆ 𝐵 ̃)⋀(𝐵 ̃ ⊆ 𝐴 ̃)]; [𝛼 ⩒ 𝛽] ∶= [¬(𝛼 → ¬𝛽)] = min (1, (𝛼) + (𝛽)); [𝛼 ⩑ 𝛽] ∶= [¬𝛼 → 𝛽)] = 𝑚𝑎𝑥(0, [𝛼] + [𝛽] − 1). Second, we give the following definitions and results in fuzzifying topology which are useful in the rest of the present paper. Definition 1. (cf. [𝟕]) Let X be a set. If 𝓅𝜖𝓅(𝑃(𝑋)) satisfies the following conditions: (1) 𝓅(X) = 𝓅(ϕ) = 1; (2) For any A, B,𝓅(𝐴 ∩ 𝐵) ≥ 𝓅(𝐴) ∧ 𝓅(𝐵); (3) For any {𝐴𝜆: 𝜆 𝜖𝐴},𝓅(⋃ 𝐴𝜆 𝜆∈𝐴 ) ≥ ⋀𝜆𝜖𝐴𝓅(𝐴𝜆). Then 𝓅 is called a fuzzifying topology, (X, 𝓅)is called a fuzzifying topological space, A𝜖𝑃(𝑋) with 𝓅(𝐴) > 0 is called a fuzzifying open set (write as 𝐴𝜖 𝓅), and 𝓅(𝐴) is called the degree of open of A. Definition 2. (cf.[𝟕, 𝟐𝟑]) Let (X, 𝓅) be a fuzzifying topological space. Then (1) 𝓅𝜖𝓅(𝑃(𝑋)), defined by 𝓅𝑐(𝐴) = 𝓅(𝑋~𝐴)(∀𝐴 ∈ 𝑃(𝑋)), is called a fuzzifying cotopology, B∈ 𝑃(𝑋) with 𝓅(𝑋~𝐴) > 0 is called a fuzzifying closed set (write as 𝐵𝜖 𝓅𝑐 ), and 𝓅𝑐 (𝐴) is called the degree of close of A, where X~A is the complement of A. (2) 𝒩 𝑥 ∈ 𝓅(𝑃(𝑋)) defined by 𝒩 𝑥(A)= ⋁ 𝓅(𝐵) 𝑥𝜖𝐵⊆𝐴 (∀𝐴𝜖𝑃(𝑋)) is called the fuzzifying neighborhood system of a point x∈X, C∈ p(X) with𝒩𝑥(C)> 0 is called a fuzzifying neighborhood of x (write as 𝐶∈ 𝒩 𝑥), and 𝒩 𝑥(𝐶) is called the degree of C being a neighborhood of x. (3) The fuzzifying cluster operator Cl(A)𝜖 𝓅(𝑋) of a set A⊆X is defined by Cl(A)(x) = 1-𝒩 𝑥(X~A) (∀𝑥 ∈ 𝑋), and Cl(A)(x) is called the degree of x being a cluster point of A. (4) The fuzzifying interior operator Int(A)∈ 𝓅(𝑋) of a set A⊆ 𝑋 is defined by Int(A)(x) = 𝒩 𝑥(𝑋~𝐴)(∀𝑥 ∈ 𝑋), and Int(A)(x) is called the degree of x being an interior point of A.
  • 2. Fuzzifying Topological System 2 Definition 3.(𝒄𝒇. [𝟖]) Let (X, 𝓅) be a fuzzifying topological space. Then (1) A ∈ P(X) satisfying the following condition is called fuzzifying e-open set (write as𝐴𝜖 𝓅𝑒): 𝓅𝑒(𝐴) = ⋀𝜆𝜖𝐴(𝐼𝑛𝑡(𝐶𝑙𝛿(𝐴)))(x)⋁ 𝐶𝑙(𝐼𝑛𝑡𝛿(𝐴))(x))> 0, Where 𝓅𝑒(A) is called the degree of e-open of A. (2) A𝜖 𝑃(𝑋) satisfying 𝓅𝑒 𝑐 (A)= 𝓅𝑒(X~A)> 0 is called a fuzzifying e-closed set (write as𝐴𝜖 𝓅𝑒 𝑐 ), and 𝓅𝑒 𝑐 (A) is called the degree of e-closed of A. (3) 𝒩 𝑥 𝑒 ∈ 𝓅(𝑃(𝑋)), defined by 𝒩𝑥 𝑒 (𝐴)=⋁𝑥∈𝐵⊆𝐴 𝓅𝑒(B) (∀𝐴 ∈ 𝑃(𝑋)) Is called a fuzzifying e-neighborhood system of a point x𝜖X, C ∈ P(X) with𝒩 𝑥 𝑒 (C)> 0 is called a fuzzifying e-neighborhood of x (write as𝐶∈ 𝒩 𝑥 𝑒 ), and 𝒩 𝑥 𝑒 (C) is called the degree of C being an e- neighborhood of x. (4) C𝑙𝑒(A) 𝜖, 𝓅(𝑋), defined by C 𝑙𝑒(A)(x)= 1 − 𝒩 𝑥 𝑒 (X~A) (∀𝑥 ∈ 𝑋), is called a fuzzifying e-cluster point operator of a subset A⊆X, x∈X with C𝑙𝑒(A)(x)> 0 is called a fuzzifying e-cluster point of A (write as 𝑥∈ 𝐶𝑙𝑒(𝐴)), and C𝑙𝑒(A)(x) is called the degree of x being a fuzzifying e-cluster point of A. (5) 𝐼𝑛𝑡𝑒(A)∈ 𝓅(𝑋), defined by 𝐼𝑛𝑡𝑒(A)(x)= 𝒩 𝑥 𝑒 (A) (∀𝑥 ∈ 𝑋), Is called a fuzzifying e-interior point operator of a subset A⊆X, x∈X with 𝑖𝑛𝑡𝑒(A)(x)> 0 is called a fuzzifying e-cluster point of A (write as𝑥𝜖 𝐼𝑛𝑡𝑒(𝐴)), and 𝐼𝑛𝑡𝑒(A)(x) is called the degree of x being fuzzifying e-interior point of A. Definition 4. (cf.[𝟖]) Let (X, 𝓅) and (Υ, 𝜇) be two fuzzifying topological spaces, 𝑓𝜖Υ𝑥 Satisfying the following condition is called fuzzifying e-continuous function (write as 𝑓𝜖 𝓅): 𝓅∈(𝑓)=⋀𝜇∈𝑝(Υ)min (1,1-𝜇(𝑈) + 𝓅𝑒(𝑓−1 (𝑈)))> 0, Where 𝓅(𝑓) is called the degree of 𝑓 being an e-continuous function. Definition 5. (cf. [𝟗]) Let (X,𝓅) and (Υ, 𝜇) be two fuzzifying topological spaces, 𝑓𝜖Υ𝑥 satisfying the following condition is called fuzzifying e-irresolute function (write as𝑓𝜖 𝓅𝑒): 𝓅𝑒(𝑓) = ⋀𝑢∈𝑝(Υ)min(1,1-𝜇𝑒(U)+ 𝓅𝑒(𝑓−1 (𝑈)) > 0, where 𝓅𝑒(𝑓) is called the degree of f being an e-irresolute function. 3. Fuzzifying e-𝜃-neighborhood system of a point, fuzzifying e-𝜃closure of a set, fuzzifying e-𝜃- interior of a set, fuzzifying e-𝜃-open sets, and fuzzifying e-𝜃-closed sets. Definition 6. 𝒩𝒙 𝒆𝜽 ∈ 𝓅(𝑃(𝑋)), defined by 𝒩 𝒙 𝒆𝜽 (A) = ⋁𝒚∈𝒑(𝑿) max (0, 𝒩 𝑥 𝑒 (B)+∧𝑦∈𝐴 𝒩𝑦 𝑒 (X~B)-1),
  • 3. Fuzzifying Topological System 3 is called a fuzzifying e-neighborhood system of a point x∈ X, C∈ P(X) with 𝒩 𝒙 𝒆𝜽 (C)> 0 is called a fuzzifying e-neighborhood of x (write as 𝐶∈ 𝒩 𝒙 𝒆𝜽 ), and 𝒩 𝒙 𝒆𝜽 (C) is called the degree of C being an e- neighborhood of x. Theorem 7. The mapping 𝒩𝑒𝜃 : 𝑋 → 𝓅𝑁 (𝑃(𝑋))( the set of all normal fuzzy subsets of P(X)), defined by 𝒩𝑒𝜃 (x)= 𝒩 𝒙 𝒆𝜽 (∀𝑥 ∈ 𝑋), has the following properties: (1) ⊨ 𝐴∈ 𝒩 𝒙 𝒆𝜽 →x∈A (∀𝑥 ∈ 𝑋 , ∀∈ 𝑃(𝑋)); (2) ⊨ A⊆B→ (𝐴∈ 𝒩 𝒙 𝒆𝜽 → 𝐵∈ 𝒩 𝒙 𝒆𝜽 )(∀𝑥 ∈ 𝑋, ∀𝐴, 𝐵 ∈ 𝑃(𝑋)); (3) ⊨ (𝐴∈ 𝒩𝒙 𝒆𝜽 )⋀( 𝐵∈ 𝒩 𝒙 𝒆𝜽 )↔ 𝐴⋂𝐵∈ 𝒩 𝒙 𝒆𝜽 (∀𝑥 ∈ 𝑋, ∀𝐴, 𝐵 ∈ 𝑃(𝑋)). Proof. (1) If[𝐴∈ 𝒩 𝒙 𝒆𝜽] = 0, then the result holds. If [𝐴∈ 𝒩 𝒙 𝒆𝜽] > 0, then ∨𝐵∈𝑃(𝑋)max (0, 𝒩𝑥 𝑒(𝐵) +∧𝑦∈𝐴 𝒩𝑦 𝑒(𝑋~𝐵) − 1) =∨𝐵∈𝑃(𝑋)max (0, 𝒩 𝑥 𝑒(𝐵) + [ C 𝑙𝑒(B) ⊆ A] − 1)> 0, and thus exists a C∈ 𝑃(𝑋) such that 𝒩 𝒙 𝒆 (C)+[C 𝑙𝑒(C) ⊆ A] − 1 > 0. From Theorems 4.2(1) and 5.5(3) in [8], we have [C 𝑙𝑒(C) ⊆ A] > 1 − 𝒩 𝒙 𝒆 (C)≥ 1 − |𝑥 ∈ 𝐶| ≥ 1 − C 𝑙𝑒(C)(x). Therefore, [C 𝑙𝑒(C) ⊆ A] = ⋀𝑦∈𝐴(1 − C 𝑙𝑒(C)(y) > 1 − C 𝑙𝑒(C)(x), and so x∈A. Otherwise, if x∈A, then ⋀𝑦∈𝐴(1 − C 𝑙𝑒(C)(y) > 1 − C 𝑙𝑒(C)(x).