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Review
32 (
6
); 2803-2809
doi:
10.1016/j.jksus.2020.07.001

m-polar fuzzy q-ideals in BCI-algebras

Department of Mathematics, University of Tabuk, Tabuk 71491, Saudi Arabia
Department of Mathematics, Shahid Beheshti University, Tehran 1983963113, Iran
Department of Mathematics Education, Gyeongsang National University, Jinju 52828, Republic of Korea

⁎Corresponding author. chishtygm@gmail.com (G. Muhiuddin), gmuhiuddin@ut.edu.sa (G. Muhiuddin),

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

In a BCI-algebra, the notion of m-polar ( , ) -fuzzy q-ideal is introduced, and its properties are investigated. Relations between m-polar ( , ) -fuzzy q-ideal and m-polar fuzzy ideal/subalgebra are discussed. Characterizations of m-polar ( , ) -fuzzy q-ideal are considered. The extension property about the m-polar ( , ) -fuzzy q-ideal is established. Homomorphic image and preimage of m-polar ( , ) -fuzzy q-ideal are discussed. Characterizations of a quasi-associative BCI-algebras are provided by using m-polar ( , ) -fuzzy q-ideal.

Keywords

m-polar fuzzy subalgebra
m-polar fuzzy ideal
m-polar ∈∈-fuzzy q-ideal
03G25
06F35
06B10
06B99
PubMed
1

1 Introduction

Fuzzy sets, which were introduced by Zadeh (1965), deal with possibilistic uncertainty, connected with imprecision of states, perceptions and preferences. After the introduction of fuzzy sets by Zadeh, fuzzy set theory has become an active area of research in various fields such as statistics, graph theory, medical and life science, engineering, business and social science, computer network, decision making, artificial intelligence, pattern recognition, robotics, and automata theory (see Kumar Singh, 2018; Feng et al., 2019; Irfan Ali et al., 2019; Irfan Ali, 2018 ). BCK/BCI-algebras, which are created from two distinct approaches: set theory and proposition calculus, first appeared in the mathematical literature in 1966 (see Imai and Iski, 1966; Iski, 1966). BCK and BCI algebras describe fragments of the propositional calculus involving implication known as BCK and BCI logics. The various attributes of BCK/BCI-algebras and their applications to different aspects are considered in Borzooei et al. (2020), Huang (2006), Meng and Jun (1994), Moussaei et al. (2018), Mohseni Takallo et al. (2019), Muhiuddin and Jun (2019, 2018), Muhiuddin and Al-roqi (2016, 2014), Muhiuddin and Aldhafeeri (2018, 2019) and Muhiuddin et al. (2014, 2017) . Ideal theory in BCI-algebras, in particular q-ideal, is studied in Liu et al. (2000). As an extension of fuzzy set, Zhang (1994) introduced the notion of bipolar fuzzy sets. Bipolar fuzzy information is applied in many (algebraic) structures, for instance, Γ -semihypergroups (see Yaqoob et al., 2014), finite state machines (see Jun and Kavikumar, 2011; Subramaniyan and Rajasekar, 2012; Yang, 2014, ), (ordered) semigroups (see Arulmozhi et al., 2019; Chinnadurai and Arulmozhi, 2018; Ibrar et al., 2019; Sardar et al., 2012 ), KU-algebras (see Muhiuddin, 2014), (hyper) BCK/BCI-algebras (see Al-Kadi and Muhiuddin, 2020; Al-Masarwah and Ahmad, 2018; Jun et al., 2012, 2011, 2009a,b ; Lee, 2009; Muhiuddin et al., 2020). In many real problems, information sometimes comes from multi-factors and there are many multi-attribute data that cannot be processed using existing anomalies (e.g., fuzzy anomalies and bipolar fuzzy anomalies, etc.). In 2014, Chen et al. (Chen et al., 2014) introduced an m-polar fuzzy set which is an extension of bipolar fuzzy set. The m-polar fuzzy models provide more precision, flexibility, and compatibility to the system when more than one agreements are to be dealt with. The m-polar fuzzy set applied to decision making problem, graph theory and BCK/BCI-algebra (Akram et al., 2019; Al-Masarwah and Ahmad, 2019; Kumar Singh, 2018; Sarwar and Akram, 2017; Al-Masarwah and Ahmad, 2019 ).

In this paper, we introduce the notion of m-polar ( , ) -fuzzy q-ideal in BCI-algebra, and investigated its properties. We discuss relations between m-polar ( , ) -fuzzy q-ideal and m-polar fuzzy ideal/subalgebra, and consider characterizations of m-polar ( , ) -fuzzy q-ideal. We establish the extension property about the m-polar ( , ) -fuzzy q-ideal. We discuss homomorphic image and preimage of m-polar ( , ) -fuzzy q-ideal, and provide characterizations of a quasi-associative BCI-algebras are provided by using m-polar ( , ) -fuzzy q-ideal.

2

2 Preliminaries

If a set X has a special element 0 and a binary operation * satisfying the conditions:

  • (I)  ( u , w , v X ) ( ( ( u * w ) * ( u * v ) ) * ( v * w ) = 0 ) ,

  • (II)  ( u , w X ) ( ( u * ( u * w ) ) * w = 0 ) ,

  • (III)  ( u X ) ( u * u = 0 ) ,

  • (IV)  ( u , w X ) ( u * w = 0 , w * u = 0 u = w ) ,

then we say that X is a BCI-algebra. If a BCI-algebra X satisfies the following identity:
  • (V)  ( u X ) ( 0 * u = 0 ) ,

then X is called a BCK-algebra. A BCI-algebra X is said to be quasi-associative if
(2.1)
( u , w , v X ) ( ( u * w ) * v u * ( w * v ) ) .
Lemma 2.1

(Huang, 2006) A BCI-algebra X is quasi-associative if and only if 0 * x = 0 * ( 0 * x ) for all x X .

Any BCK/BCI-algebra X satisfies the following conditions:

(2.2)
( u X ) u * 0 = u ,
(2.3)
( u , w , v X ) u w u * v w * v , v * w v * u ,
(2.4)
( u , w , v X ) ( u * w ) * v = ( u * v ) * w
where u w if and only if u * w = 0 . A subset S of a BCK/BCI-algebra X is called a subalgebra of X if u * w S for all u , w S . A subset I of a BCK/BCI-algebra X is called an ideal of X if it satisfies:
(2.5)
0 I ,
(2.6)
( u X ) w I u * w I u I .

A subset I of a BCI-algebra X is called

  • a p-ideal of X if it satisfies (2.5) and

    (2.7)
    ( u , w , v X ) ( u * v ) * ( w * v ) I , w I u I .

  • a q-ideal of X if it satisfies (2.5) and

    (2.8)
    ( u , w , v X ) u * ( w * v ) I , w I u * v I .

See the books Huang (2006) and Meng and Jun (1994) for more information on BCK/BCI-algeebras.

By an m-polar fuzzy set on a set X (see Chen et al., 2014), we mean a function α ̂ : X [ 0 , 1 ] m . The membership value of every element x X is denoted by α ̂ ( x ) = ( π 1 α ̂ ) ( x ) , ( π 2 α ̂ ) ( x ) , , ( π m α ̂ ) ( x ) , where π i : [ 0 , 1 ] m [ 0 , 1 ] is the i-th projection for all i = 1 , 2 , , m .

Given an m-polar fuzzy set on a set X , we consider the set

(2.9)
U ( α ̂ ; t ̂ ) { x X | α ̂ ( x ) t ̂ } , that is,
(2.10)
U ( α ̂ ; t ̂ ) { x X | ( π 1 α ̂ ) ( x ) t 1 , ( π 2 α ̂ ) ( x ) t 2 , , ( π m α ̂ ) ( x ) t m } ,
which is called an m-polar level set of α ̂ .

By an m-polar fuzzy point on a set X , we mean an m-polar fuzzy set α ̂ on X of the form

(2.11)
α ̂ ( y ) = r ̂ = ( r 1 , r 2 , , r m ) 0 , 1 m if y = x , 0 ̂ = ( 0 , 0 , , 0 ) if y x , and it is denoted by x r ̂ . We say that x is the support of x r ̂ and r ̂ is the value of x r ̂ .

We say that an m-polar fuzzy point x r ̂ is contained in an m-polar fuzzy set α ̂ , denoted by x r ̂ α ̂ , if α ̂ ( x ) r ̂ , that is, ( π i α ̂ ) ( x ) r i for all i = 1 , 2 , , m .

Definition 2.2

(Al-Masarwah and Ahmad, 2019, Definition 3.1) An m-polar fuzzy set α ̂ on a BCK/BCI-algebra X is called an m-polar fuzzy subalgebra of X if the following condition is valid.

(2.12)
( x , y X ) α ̂ ( x * y ) inf { α ̂ ( x ) , α ̂ ( y ) } , that is,
(2.13)
( x , y X ) ( π i α ̂ ) ( x * y ) inf { ( π i α ̂ ) ( x ) , ( π i α ̂ ) ( y ) }
for all i = 1 , 2 , , m .

Example 2.3

Let X = { 0 , a , b , c } be a BCK-algebra with a Cayley table which is appeared in Table 1.

Define a 4-polar fuzzy set α ̂ on X as follows; α ̂ : X [ 0 , 1 ] 4 , x ( 0.33 , 0.41 , 0.57 , 0.83 ) if x = 0 , ( 0.25 , 0.33 , 0.42 , 0.38 ) if x = a , ( 0.22 , 0.30 , 0.40 , 0.20 ) if x = b , ( 0.25 , 0.34 , 0.55 , 0.50 ) if x = c

It is routine to check that α ̂ is a 4-polar fuzzy subalgebra of X .

Definition 2.4

(Al-Masarwah and Ahmad, 2019, Definition 3.7) An m-polar fuzzy set α ̂ on a BCK/BCI-algebra X is called an m-polar fuzzy ideal of X if the following conditions are valid.

(2.14)
( x X ) α ̂ ( 0 ) α ̂ ( x ) ,
(2.15)
( x , y X ) α ̂ ( x ) inf { α ̂ ( x * y ) , α ̂ ( y ) } ,
that is,
(2.16)
( x X ) ( π i α ̂ ) ( 0 ) ( π i α ̂ ) ( x ) ,
(2.17)
( x , y X ) ( π i α ̂ ) ( x ) inf { ( π i α ̂ ) ( x * y ) , ( π i α ̂ ) ( y ) }
for all i = 1 , 2 , , m .

Example 2.5

Let X = { 0 , a , b , c , d } be a BCI-algebra with a Cayley table which is appeared in Table 2.

Define a 4-polar fuzzy set α ̂ on X as follows; α ̂ : X [ 0 , 1 ] 4 , x ( 0.50 , 0.60 , 0.60 , 0.70 ) if x = 0 , ( 0.40 , 0.50 , 0.50 , 0.70 ) if x = a , ( 0.20 , 0.30 , 0.30 , 0.20 ) if x = b , d , ( 0.30 , 0.40 , 0.40 , 0.50 ) if x = c

It is routine to check that α ̂ is a 4-polar fuzzy ideal of X .

Lemma 2.6

Lemma 2.6 Mohseni Takallo et al., 2019, Lemma 1

An m-polar fuzzy set α ̂ on a BCK/BCI-algebra X is an m-polar fuzzy ideal of X if and only if the following conditions are valid.

(2.18)
( x X ) ( r ̂ [ 0 , 1 ] m ) x r ̂ α ̂ 0 r ̂ α ̂ ,
(2.19)
( x , y X ) ( r ̂ , t ̂ [ 0 , 1 ] m ) ( x * y ) r ̂ α ̂ , y t ̂ α ̂ x inf { r ̂ , t ̂ } α ̂ .

Definition 2.7

An m-polar fuzzy set α ̂ on a BCI-algebra X is called an m-polar ( , ) -fuzzy p-ideal of X if it satisfies (2.18) and

(2.20)
( x , y , z X ) ( r ̂ , t ̂ [ 0 , 1 ] m ) ( ( x * z ) * ( y * z ) ) r ̂ α ̂ , y t ̂ α ̂ x inf { r ̂ , t ̂ } α ̂ .

Table 1 Cayley table for the binary operation “ * ”.
* 0 a b c
0 0 0 0 0
a a 0 0 a
b b a 0 b
c c c c 0
Table 2 Cayley table for the binary operation “ * ”.
* 0 a b c d
0 0 0 d c b
a a 0 d c b
b b b 0 d c
c c c b 0 d
d d d c b 0

Note that the condition (2.20) is equivalent to the following condition.

(2.21)
( x , y , z X ) α ̂ ( x ) inf α ̂ ( ( x * z ) * ( y * z ) ) , α ̂ ( y ) , that is,
(2.22)
( π i α ̂ ) ( x ) inf { ( π i α ̂ ) ( ( x * z ) * ( y * z ) ) , ( π i α ̂ ) ( y ) }
for all x , y , z X and i = 1 , 2 , , m .
Example 2.8

Let X = { 0 , 1 , 2 , 3 } be a set with a binary operation * which is given in Table 3.

Then X is a BCI-algebra (see Huang, 2006). Define a 5-polar fuzzy set α ̂ on X as follows: α ̂ : X [ 0 , 1 ] 5 , x ( 0.7 , 0.6 , 0.8 , 0.5 , 0.9 ) if x = 0 , ( 0.5 , 0.6 , 0.7 , 0.4 , 0.7 ) if x = 1 , ( 0.3 , 0.4 , 0.6 , 0.2 , 0.5 ) if x = 2 , ( 0.3 , 0.4 , 0.6 , 0.2 , 0.5 ) if x = 3

It is routine to check that α ̂ is a 5-polar ( , ) -fuzzy p-ideal of X .

Table 3 Cayley table for the binary operation “ * ”.
* 0 1 2 3
0 0 1 2 3
1 1 0 3 2
2 2 3 0 1
3 3 2 1 0

3

3 m-polar fuzzy q-ideals

Definition 3.1

An m-polar fuzzy set α ̂ on a BCI-algebra X is called an m-polar ( , ) -fuzzy q-ideal of X if it satisfies (2.18) and

(3.1)
( x , y , z X ) ( r ̂ , t ̂ [ 0 , 1 ] m ) ( x * ( y * z ) ) r ̂ α ̂ , y t ̂ α ̂ ( x * z ) inf { r ̂ , t ̂ } α ̂ .

It is routine to verify that the condition (3.1) is equivalent to the following condition.

(3.2)
( x , y , z X ) α ̂ ( x * z ) inf { α ̂ ( x * ( y * z ) ) , α ̂ ( y ) } , that is,
(3.3)
( π i α ̂ ) ( x * z ) inf { ( π i α ̂ ) ( x * ( y * z ) ) , ( π i α ̂ ) ( y ) }
for all x , y , z X and i = 1 , 2 , , m .
Example 3.2

Let X = { 0 , 1 , a } be a set with a binary operation * which is given in Table 4.

Then X is a BCI-algebra (see Huang, 2006). Define a 3-polar fuzzy set α ̂ on X as follows: α ̂ : X [ 0 , 1 ] 3 , x ( 0.75 , 0.63 , 0.82 ) if x = 0 , ( 0.55 , 0.63 , 0.72 ) if x = 1 , ( 0.35 , 0.43 , 0.62 ) if x = a

It is routine to check that α ̂ is a 3-polar ( , ) -fuzzy q-ideal of X .

Theorem 3.3

Every m-polar ( , ) -fuzzy q-ideal of a BCI-algebra X is an m-polar fuzzy ideal and an m-polar fuzzy subalgebra of X .

Proof

Let α ̂ be an m-polar ( , ) -fuzzy q-ideal of a BCI-algebra X . Putting z = 0 in (3.3) and using (2.2) implies that ( π i α ̂ ) ( x ) = ( π i α ̂ ) ( x * 0 ) inf { ( π i α ̂ ) ( x * ( y * 0 ) ) , ( π i α ̂ ) ( y ) } = inf { ( π i α ̂ ) ( x * y ) ) , ( π i α ̂ ) ( y ) } for all x , y X and i = 1 , 2 , , m . Hence α ̂ is an m-polar fuzzy ideal of X . Putting z = y in (3.3) and using (III) and (2.2) implies that ( π i α ̂ ) ( x * y ) inf { ( π i α ̂ ) ( x * ( y * y ) ) , ( π i α ̂ ) ( y ) } = inf { ( π i α ̂ ) ( x * 0 ) , ( π i α ̂ ) ( y ) } = inf { ( π i α ̂ ) ( x ) , ( π i α ̂ ) ( y ) } for all x , y X and i = 1 , 2 , , m . Thus α ̂ is an m-polar fuzzy subalgebra of X . □

Table 4 Cayley table for the binary operation “ * ”.
* 0 1 a
0 0 0 a
1 1 0 a
a a a 0

In the following example, we show that the converse of Theorem 3.3 is not true in general.

Example 3.4

Let X = { 0 , 1 , b , c } be a set with a binary operation * which is given in Table 5.

Then X is a BCI-algebra (see Huang, 2006). Define a 4-polar fuzzy set α ̂ on X as follows: α ̂ : X [ 0 , 1 ] 4 , x ( 0.7 , 0.6 , 0.8 , 0.5 ) if x = 0 , ( 0.3 , 0.4 , 0.5 , 0.2 ) if x = 1 , ( 0.3 , 0.5 , 0.6 , 0.2 ) if x = b , ( 0.3 , 0.4 , 0.5 , 0.2 ) if x = c

It is routine to check that α ̂ is an 4-polar fuzzy ideal and a 4-polar fuzzy subalgebra of X . But it is not a 4-polar ( , ) -fuzzy q-ideal of X since ( π 2 α ̂ ) ( c * 1 ) = ( π 2 α ̂ ) ( b ) = 0.5 < 0.6 = inf { ( π 2 α ̂ ) ( c * ( 0 * 1 ) ) , ( π 2 α ̂ ) ( 0 ) } .

Table 5 Cayley table for the binary operation “ * ”.
* 0 1 b c
0 0 c b 1
1 1 0 c b
b b 1 0 c
c c b 1 0

We provide conditions for an m-polar fuzzy ideal to be an m-polar ( , ) -fuzzy q-ideal, and consider characterization of m-polar ( , ) -fuzzy q-ideal.

Lemma 3.5

(Al-Masarwah and Ahmad, 2019, Proposition 3.9) If α ̂ is an m-polar fuzzy ideal of a BCI-algebra X , then ( x , y X ) ( x y α ̂ ( x ) α ̂ ( y ) ) , that is, ( π i α ̂ ) ( x ) ( π i α ̂ ) ( y ) for all x , y X with x y and i = 1 , 2 , , m .

Lemma 3.6

(Al-Masarwah and Ahmad, 2019, Proposition 3.14) If α ̂ is an m-polar fuzzy ideal of a BCI-algebra X , then ( x , y , z X ) ( x * y z α ̂ ( x ) inf { α ̂ ( y ) , α ̂ ( z ) } ) , that is, ( π i α ̂ ) ( x ) inf { ( π i α ̂ ) ( y ) , ( π i α ̂ ) ( z ) } for all x , y , z X with x * y z and i = 1 , 2 , , m .

Theorem 3.7

Given an m-polar fuzzy ideal α ̂ of a BCI-algebra X , the following are equivalent.

  • (1)  α ̂ is an m-polar ( , ) -fuzzy q-ideal of X .

  • (2)  α ̂ satisfies α ̂ ( x * y ) α ̂ ( x * ( 0 * y ) ) , that is, ( π i α ̂ ) ( x * y ) ( π i α ̂ ) ( x * ( 0 * y ) ) for all x , y X and i = 1 , 2 , , m .

  • (3)  α ̂ satisfies α ̂ ( ( x * y ) * z ) α ̂ ( x * ( y * z ) ) , that is, ( π i α ̂ ) ( ( x * y ) * z ) ( π i α ̂ ) ( x * ( y * z ) ) for all x , y , z X and i = 1 , 2 , , m .

Proof

(1) (2). If we replace y and z by 0 and y , respectively, in (3.3) and use (2.16), then ( π i α ̂ ) ( x * y ) inf { ( π i α ̂ ) ( x * ( 0 * y ) ) , ( π i α ̂ ) ( 0 ) } = ( π i α ̂ ) ( x * ( 0 * y ) ) , and so for α ̂ ( x * y ) α ̂ ( x * ( 0 * y ) ) for all x , y X .(2) (3). Note that ( ( x * y ) * ( 0 * z ) ) * ( x * ( y * z ) ) = ( ( x * y ) * ( x * ( y * z ) ) ) * ( 0 * z ) ( ( y * z ) * y ) * ( 0 * z ) = ( 0 * z ) * ( 0 * z ) = 0 , that is, ( x * y ) * ( 0 * z ) x * ( y * z ) for all x , y , z X . It follows from (2) and Lemma 3.5 that ( π i α ̂ ) ( ( x * y ) * z ) ( π i α ̂ ) ( ( x * y ) * ( 0 * z ) ) ( π i α ̂ ) ( x * ( y * z ) ) , that is, α ̂ ( ( x * y ) * z ) α ̂ ( x * ( y * z ) ) for all x , y , z X .

(3) (1). Note that ( x * z ) * ( ( x * y ) * z ) x * ( x * y ) y for all x , y , z X . Using (3) and Lemma 3.6, we have ( π i α ̂ ) ( x * z ) inf { ( π i α ̂ ) ( ( x * y ) * z ) , ( π i α ̂ ) ( y ) } inf { ( π i α ̂ ) ( x * ( y * z ) ) , ( π i α ̂ ) ( y ) } and so α ̂ ( x * z ) inf { α ̂ ( x * ( y * z ) ) , α ̂ ( y ) } for all x , y , z X . Therefore α ̂ is an m-polar ( , ) -fuzzy q-ideal of X . □

Theorem 3.8

An m-polar fuzzy set α ̂ on a BCI-algebra X is an m-polar ( , ) -fuzzy q-ideal of X if and only if the m-polar level set U ( α ̂ ; r ̂ ) of α ̂ is a q-ideal of X for all r ̂ [ 0 , 1 ] m .

Proof

Suppose that α ̂ is an m-polar ( , ) -fuzzy q-ideal of X and let r ̂ = ( r 1 , r 2 , , r m ) 0 , 1 m . It is clear that 0 U ( α ̂ ; r ̂ ) . Let x , y , z X be such that x * ( y * z ) U ( α ̂ ; r ̂ ) and y U ( α ̂ ; r ̂ ) . Then ( π i α ̂ ) ( x * ( y * z ) ) r i and ( π i α ̂ ) ( y ) r i for all i = 1 , 2 , , m . It follows from (3.3) that ( π i α ̂ ) ( x * z ) inf { ( π i α ̂ ) ( x * ( y * z ) ) , ( π i α ̂ ) ( y ) } r i for i = 1 , 2 , , m . Hence x * z U ( α ̂ ; r ̂ ) , and therefore U ( α ̂ ; r ̂ ) is a q-ideal of X .

Conversely, suppose that the m-polar level set U ( α ̂ ; r ̂ ) of α ̂ is a q-ideal of X for all r ̂ [ 0 , 1 ] m . If α ̂ ( 0 ) < α ̂ ( a ) for some a X and take r ̂ α ̂ ( a ) , then a U ( α ̂ ; r ̂ ) and 0 U ( α ̂ ; r ̂ ) . This is a contradiction, and so α ̂ ( 0 ) α ̂ ( x ) for all x X . Now, suppose that there exist a , b , c X such that α ̂ ( a * c ) < inf { α ̂ ( a * ( b * c ) ) , α ̂ ( b ) } . If we take r ̂ inf { α ̂ ( a * ( b * c ) ) , α ̂ ( b ) } , then a * ( b * c ) U ( α ̂ ; r ̂ ) and b U ( α ̂ ; r ̂ ) . Since U ( α ̂ ; r ̂ ) is a q-ideal of X , it follows that a * c U ( α ̂ ; r ̂ ) . Hence α ̂ ( a * c ) r ̂ , which is a contradiction. Thus α ̂ ( x * z ) inf { α ̂ ( x * ( y * z ) ) , α ̂ ( y ) } for all x , y , z X . Therefore α ̂ is an m-polar ( , ) -fuzzy q-ideal of X . □

Corollary 3.9

If α ̂ is an m-polar ( , ) -fuzzy q-ideal of a BCI-algebra X , then the set J { x X | α ̂ ( x ) = α ̂ ( 0 ) } is a q-ideal of X .

We give an extension property about the m-polar ( , ) -fuzzy q-ideal of a BCI-algebra X .

Theorem 3.10

Let α ̂ and β ̂ be m-polar fuzzy ideals of a BCI-algebra X such that α ̂ β ̂ and α ̂ ( 0 ) = β ̂ ( 0 ) . If α ̂ is an m-polar ( , ) -fuzzy q-ideal of X , then so is β ̂ .

Proof

Assume that α ̂ is an m-polar ( , ) -fuzzy q-ideal of X . Since ( x * ( x * ( 0 * y ) ) ) * ( 0 * y ) = ( x * ( 0 * y ) ) * ( x * ( 0 * y ) ) = 0 for all x , y X , we have ( π i α ̂ ) ( ( x * ( x * ( 0 * y ) ) ) * ( 0 * y ) ) = ( π i α ̂ ) ( 0 ) = ( π i β ̂ ) ( 0 ) for i = 1 , 2 , , m . It follows from Theorem 3.7 that ( π i α ̂ ) ( ( x * ( x * ( 0 * y ) ) ) * y ) ( π i α ̂ ) ( ( x * ( x * ( 0 * y ) ) ) * ( 0 * y ) ) = ( π i β ̂ ) ( 0 ) .

Hence ( π i β ̂ ) ( ( x * y ) * ( x * ( 0 * y ) ) ) = ( π i β ̂ ) ( ( x * ( x * ( 0 * y ) ) ) * y ) ( π i α ̂ ) ( ( x * ( x * ( 0 * y ) ) ) * y ) ( π i β ̂ ) ( 0 ) ( π i β ̂ ) ( x * ( 0 * y ) ) , which implies from (2.17) that ( π i β ̂ ) ( x * y ) inf { ( π i β ̂ ) ( ( x * y ) * ( x * ( 0 * y ) ) ) , ( π i β ̂ ) ( x * ( 0 * y ) ) } = ( π i β ̂ ) ( x * ( 0 * y ) ) for all x , y X and i = 1 , 2 , , m . Therefore β ̂ is an m-polar ( , ) -fuzzy q-ideal of X by Theorem 3.7. □

Theorem 3.11

Let f : X Y be an epimorphism of BCI-algebras. If β ̂ is an m-polar ( , ) -fuzzy q-ideal of Y , then the m-polar fuzzy set α ̂ on X defined by α ̂ : X [ 0 , 1 ] m , x β ̂ ( f ( x ) ) , that is, ( π i α ̂ ) ( x ) = ( π i β ̂ ) ( f ( x ) ) for x X and i = 1 , 2 , , m is an m-polar ( , ) -fuzzy q-ideal of X .

Proof

Let β ̂ be an m-polar ( , ) -fuzzy q-ideal of Y . For any x X , we have ( π i α ̂ ) ( x ) = ( π i β ̂ ) ( f ( x ) ) ( π i β ̂ ) ( 0 ) = ( π i β ̂ ) ( f ( 0 ) ) = ( π i α ̂ ) ( 0 ) for all i = 1 , 2 , , m . Let x , y , z X . Then ( π i α ̂ ) ( x * z ) = ( π i β ̂ ) ( f ( x * z ) ) = ( π i β ̂ ) ( f ( x ) * f ( y ) ) inf { ( π i β ̂ ) ( f ( x ) * ( f ( y ) * f ( z ) ) ) , ( π i β ̂ ) ( f ( y ) ) } = inf { ( π i β ̂ ) ( f ( x * ( y * z ) ) ) , ( π i β ̂ ) ( f ( y ) ) } = inf { ( π i α ̂ ) ( x * ( y * z ) ) , ( π i α ̂ ) ( y ) } for all i = 1 , 2 , , m . Therefore α ̂ is an m-polar ( , ) -fuzzy q-ideal of X . □

Theorem 3.12

Let f : X Y be an epimorphism of BCI-algebras. If α ̂ is an m-polar ( , ) -fuzzy q-ideal of X such that ( T X ) ( x 0 T ) α ̂ ( x 0 ) = sup a T α ̂ ( a ) , then the image β ̂ of α ̂ under f which is defined by β ̂ : Y [ 0 , 1 ] m , y sup x f - 1 ( y ) α ̂ ( x ) is an m-polar ( , ) -fuzzy q-ideal of Y .

Proof

Since 0 f - 1 ( 0 ) , we have β ̂ ( 0 ) = sup x f - 1 ( 0 ) α ̂ ( x ) = α ̂ ( 0 ) α ̂ ( x ) for all x X , and so β ̂ ( 0 ) = sup x f - 1 ( y ) α ̂ ( x ) = β ̂ ( y ) for all y Y . For any a , b , c Y , let x 0 f - 1 ( a ) , y 0 f - 1 ( b ) and z 0 f - 1 ( c ) satisfying α ̂ ( x 0 * z 0 ) = sup u f - 1 ( a * c ) α ̂ ( u ) , α ̂ ( y 0 ) = sup u f - 1 ( b ) α ̂ ( u ) and α ̂ ( x 0 * ( y 0 * z 0 ) ) = sup u f - 1 ( a * ( b * c ) ) α ̂ ( u ) . Then β ̂ ( a * c ) = sup u f - 1 ( a * c ) α ̂ ( u ) = α ̂ ( x 0 * z 0 ) inf { α ̂ ( x 0 * ( y 0 * z 0 ) ) , α ̂ ( y 0 ) } = inf sup u f - 1 ( a * ( b * c ) ) α ̂ ( u ) , sup u f - 1 ( b ) α ̂ ( u ) } = inf β ̂ ( a * ( b * c ) ) , β ̂ ( b ) .

Therefore β ̂ is an m-polar ( , ) -fuzzy q-ideal of Y . □

Lemma 3.13

Let I be a subset of a BCI-algebra X and let α ̂ I be an m-polar fuzzy set on X defined by α ̂ I : X [ 0 , 1 ] m , x 1 ̂ if x I , 0 ̂ otherwise

Then α ̂ I is an m-polar ( , ) -fuzzy ideal (resp., m-polar ( , ) -fuzzy q-ideal) of X if and only if I is an ideal (resp., q-ideal) of X .

Proof

Straightforward. □

We provide characterizations of a quasi-associative BCI-algebras.

Theorem 3.14

Given a BCI-algebra X , the following assertions are equivalent.

  • (1)  X is quasi-associative.

  • (2) Every m-polar fuzzy ideal of X is an m-polar ( , ) -fuzzy q-ideal of X .

  • (3) Every m-polar fuzzy ideal α ̂ of X with α ̂ ( 0 ) = 1 ̂ is an m-polar ( , ) -fuzzy q-ideal of X .

  • (4) Every zero m-polar fuzzy ideal α ̂ { 0 } of X is an m-polar ( , ) -fuzzy q-ideal of X .

  • (5) Every m-polar fuzzy ideal α ̂ X + of X is an m-polar ( , ) -fuzzy q-ideal of X , where X + is the BCK-part of X .

  • (6) The m-polar fuzzy ideal β ̂ of X with β ̂ α ̂ X + and β ̂ ( 0 ) = 1 ̂ is an m-polar ( , ) -fuzzy q-ideal of X .

Proof

(1) (2). Let α ̂ be m-polar fuzzy ideal of X . Using (2.1) and Lemma 3.5, we have α ̂ ( ( x * y ) * z ) α ̂ ( x * ( y * z ) ) for all x , y , z X . It follows from (2.15) and (2.4) that α ̂ ( x * z ) inf { α ̂ ( ( x * z ) * y ) , α ̂ ( y ) } = inf { α ̂ ( ( x * y ) * z ) , α ̂ ( y ) } inf { α ̂ ( x * ( y * z ) ) , α ̂ ( y ) } for all x , y , z X . Hence α ̂ is an m-polar ( , ) -fuzzy q-ideal of X .

(2) (3), (3) (4) and (2) (6) are straighrforward.

(4) (5). Note that α ̂ { 0 } α ̂ X + and α ̂ { 0 } ( 0 ) = 1 ̂ = α ̂ X + ( 0 ) . Thus α ̂ X + is an m-polar ( , ) -fuzzy q-ideal of X by Theorem 3.10.

(5) (1). If α ̂ X + is an m-polar ( , ) -fuzzy q-ideal of X , then X + is a q-ideal of X by Lemma 3.13. Since ( 0 * x ) * ( 0 * x ) = 0 X + for all x X , it follows from (2.8) that ( 0 * x ) * x X + . Hence 0 * x = 0 * ( 0 * x ) for all x X , and so X is quasi-associative by Lemma 2.1.

(6) (5). Since β ̂ α ̂ X + and β ̂ ( 0 ) = 1 ̂ = α ̂ X + ( 0 ) , we know that α ̂ X + is an m-polar ( , ) -fuzzy q-ideal of X by Theorem 3.10. □

Corollary 3.15

If a BCI-algebra X meets any of the following conditions

  • (1)  ( x , y X ) ( 0 * ( x * y ) = 0 * ( y * x ) ) ,

  • (2)  ( x , y X ) ( ( 0 * x ) * y = 0 * ( x * y ) ) ,

  • (3)  ( x , y X ) ( x * ( 0 * y ) = 0 x * y = 0 ) ,

  • (4)  ( x X ) ( S x { 0 , 0 * x } isasubalgebraofX ) ,

  • (5) The p-semisimple part of X is an associative subalgebra of X ,

then X is a quasi-associative and so has all of five other properties of Theorem 3.14 .

Proof

By Lemma 2.1, a BCI-algebra X is quasi-associative if and only if 0 * x = 0 * ( 0 * x ) , for all x X . Now, it is easy to prove that each of the properties (1) to (5) are equivalent to the condition 0 * x = 0 * ( 0 * x ) , for all x X . Hence by Lemma 2.1, X is a quasi-associative and so by Theorem 3.14, X has all of five other properties of Theorem 3.14. □

4

4 Conclusions

The traditional fuzzy set expression cannot distinguish between elements unrelated to the opposite. It is difficult to express differences in components unrelated to the opposing elements in the fuzzy set only if the membership extends over the interval [0, 1]. If a set expression can express this kind of difference, it will be more beneficial than a traditional fuzzy set expression. Based on these observations, Lee introduced an extension of the fuzzy set called the bipolar value fuzzy set in his paper [Lee, K.M. Bipolar-valued fuzzy sets and their operations. Proc. Int. Conf. on Intelligent Technologies, Bangkok, Thailand 2000, 307–312]. This concept is being applied from various angles to algebraic structure and applied science etc. An m-polar fuzzy model is a generalized form of a bipolar fuzzy model. The m-polar fuzzy models provide more precision, flexibility and compatibility to the system when more than one agreements are to be dealt with. The purpose of this paper is to study m-polar fuzzy q-ideals of BCI-algebras. We have first introduced the notion of m-polar ( , ) -fuzzy q-ideals of BCI-algebras and have investigated several properties. we have discussed relations between an m-polar fuzzy ideal/subalgebra and an m-polar ( , ) -fuzzy q-ideal, and considered the characterization of m-polar ( , ) -fuzzy q-ideal. We discussed homomorphic image and preimage of m-polar ( , ) -fuzzy q-ideal, and provided characterizations of a quasi-associative BCI-algebras are provided by using m-polar ( , ) -fuzzy q-ideal. There are several kinds of ideals in BCI-algebras, for example, p-ideal, q-ideal, a-ideal, BCI-implicative ideal, BCI-positive implicative ideal, BCI-commutative ideal, sub-implicative ideal, etc. These different kinds of ideals are basically very relevant to the ideal. Thus, the polarity of q-ideals as studied in this paper will be the basic step in the polarity study of other ideals.

The purpose of our research in the future is study on set of all m-polar fuzzy q-ideals of BCI-algebras. Can we construct a lattice structure on this set? Can we define an algebraic structure on this set such that it be a BCI-algebra again?.

Acknowledgment

The authors are grateful to the anonymous referees for a careful checking of the details and for helpful comments that improved the overall presentation of this paper.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. , , , . Novel approach in decision making with m-polar fuzzy ELECTRE-I. Int. J. Fuzzy Syst.. 2019;21(4):1117-1129.
    [Google Scholar]
  2. , , . Bipolar fuzzy BCI-implicative ideals of BCI-algebras. Ann. Commun. Math.. 2020;3:88-96.
    [Google Scholar]
  3. , , . m-polar fuzzy ideals of BCK/BCI-algebras. J. King Saud Univ.-Sci.. 2019;31:1220-1226.
    [Google Scholar]
  4. , , . A new form of generalized m-PF Ideals in BCK/BCI-algebras. Ann. Commun. Math.. 2019;2(1):11-16.
    [Google Scholar]
  5. , , . Doubt bipolar fuzzy subalgebras and ideals in BCK/BCI-algebras. J. Math. Anal.. 2018;9(3):9-27.
    [Google Scholar]
  6. , , , . Interval valued bipolar fuzzy ideals in ordered Γ)semigroups. J. Int. Math. Virtual Inst.. 2019;9:1-17.
    [Google Scholar]
  7. , . m-polar fuzzy graph representation of concept lattice. Eng. Appl. Artif. Intell.. 2018;67:52-62.
    [Google Scholar]
  8. , , , . Polarity of generalized neutrosophic subalgebras in BCK/BCI-algebras. Neutros. Sets Syst.. 2020;32:123-145.
    [Google Scholar]
  9. , , , , . m-polar fuzzy sets: An extension of bipolar fuzzy sets. Sci. World J.. 2014;2014:416530
    [Google Scholar]
  10. , , . Characterization of bipolar fuzzy ideals in ordered gamma semigroups. J. Int. Math. Virtual Inst.. 2018;8:141-156.
    [Google Scholar]
  11. , , , , , . Another view on generalized intuitionistic fuzzy soft sets and related multiattribute decision making methods. IEEE Trans. Fuzzy Syst.. 2019;27(3):474-488.
    [Google Scholar]
  12. , . BCI-Algebra . Beijing, China: Science Press; .
  13. , , , . Generalized bipolar fuzzy interior ideals in ordered semigroups. Honam Math. J.. 2019;41(2):285-300.
    [Google Scholar]
  14. , , , , , . A graphical method for ranking Atanassov’s intuitionistic fuzzy values using the uncertainty index and entropy. Int. J. Intell. Syst.. 2019;34:2692-2712.
    [Google Scholar]
  15. , . Another view on qrung orthopair fuzzy sets. Int. J. Intell. Syst.. 2018;33:2139-2153.
    [Google Scholar]
  16. , , . On axiom systems of propositional calculi. Proc. Jpn. Acad.. 1966;42:19-21.
    [Google Scholar]
  17. , . An algebra related with a propositional calculus. Proc. Jpn. Acad.. 1966;42:26-29.
    [Google Scholar]
  18. , , . Bipolar fuzzy finite state machines. Bull. Malays. Math. Sci. Soc.. 2011;34(1):181-188.
    [Google Scholar]
  19. , , , . Several types of bipolar fuzzy hyper BCK-ideals in hyper BCK-algebras. Honam. Math. J.. 2012;34(2):145-159.
    [Google Scholar]
  20. , , , . Bipolar fuzzy hyper BCK-ideals in hyper BCK-algebras. Iran. J. Fuzzy Syst.. 2011;8(2):105-120.
    [Google Scholar]
  21. , , , . Bipolar fuzzy structures of some types of ideals in hyper BCK-algebras. Sci. Math. Jpn.. 2009;70(1):109-121.
    [Google Scholar]
  22. , , , . Bipolar fuzzy implicative hyper BCK-ideals in hyper BCK-algebras. Sci. Math. Jpn.. 2009;69(2):175-186.
    [Google Scholar]
  23. , . Bipolar fuzzy subalgebras and bipolar fuzzy ideals of BCK/BCI-algebras. Bull. Malays. Math. Sci. Soc.. 2009;32(3):361-373.
    [Google Scholar]
  24. , , , , . q-Ideals and a-Ideals in BCI-Algebras. Southeast Asian Bull. Math.. 2000;24:243-253.
    [Google Scholar]
  25. , , . BCK-Algebras . Seoul, Korea: Kyungmoonsa Co.; .
  26. , , , . P-closure ideals in BCI-algebras. Soft Comput.. 2018;22(23):7901-7908.
    [Google Scholar]
  27. , , , , . Multipolar fuzzy p-ideals of BCI-algebras. Mathematics. 2019;7(11)
    [CrossRef] [Google Scholar]
  28. , , . Sup-hesitant fuzzy subalgebras and its translations and extensions. Ann. Commun. Math.. 2019;2:48-56.
    [Google Scholar]
  29. , , . p-semisimple neutrosophic quadruple BCI-algebras and neutrosophic quadruple p-ideals. Ann. Commun. Math.. 2018;1:26-37.
    [Google Scholar]
  30. , , . Classifications of (alpha, beta)-fuzzy ideals in BCK / BCI –algebras. J. Math. Anal.. 2016;7(6):75-82.
    [Google Scholar]
  31. , , . Cubic soft sets with applications in BCK / BCI -algebras. Ann. Fuzzy Math. Inform.. 2014;8:291-304.
    [Google Scholar]
  32. , , . Subalgebras and ideals in BCK / BCI -algebras based on uni-hesitant fuzzy set theory. Eur. J. Pure Appl. Math.. 2018;11:417-430.
    [Google Scholar]
  33. , , . N-Soft p-ideal of BCI-algebras. Eur. J. Pure Appl. Math.. 2019;12:79-87.
    [Google Scholar]
  34. , , , . Subalgebras of BCK / BCI -algebras based on cubic soft sets. Sci. World J.. 2014;458638
    [CrossRef] [Google Scholar]
  35. , , , , . Hesitant fuzzy translations and extensions of subalgebras and ideals in BCK/BCI)algebras. J. Intell. Fuzzy Syst.. 2017;32:43-48.
    [Google Scholar]
  36. , , , . Bipolar-valued fuzzy soft hyper BCK ideals in hyper BCK algebras. Discrete Math. Algorithms Appl.. 2020;12(02):2050018.
    [Google Scholar]
  37. , . Bipolar fuzzy KU-subalgebras/ideals of KU-algebras. Ann. Fuzzy Math. Inf.rmatics. 2014;8(3):409-418.
    [Google Scholar]
  38. , , . Representation of graphs using m-polar fuzzy environment. Ital. J. Pure Appl. Math.. 2017;38:291-312.
    [Google Scholar]
  39. , , , . Bipolar valued fuzzy translation in semigroups. Math. Aeterna. 2012;2(7–8):597-607.
    [Google Scholar]
  40. , , . Homomorphism in bipolar fuzzy finite state machines. Int. Math. Forum. 2012;7(29–32):1505-1516.
    [Google Scholar]
  41. , . Algebraic characterizations of a bipolar fuzzy finite state machine (Chinese) Mohu Xitong yu Shuxue. 2014;28(1):46-52.
    [Google Scholar]
  42. , . Semigroups of bipolar fuzzy finite state machines (Chinese) Mohu Xitong yu Shuxue. 2014;28(2):86-90.
    [Google Scholar]
  43. , , , , . Structures of bipolar fuzzy G-hyperideals in G-semihypergroups. J. Intell. Fuzzy Syst.. 2014;27:3015-3032.
    [Google Scholar]
  44. , . Fuzzy sets. Inform. Control. 1965;8:338-353.
    [Google Scholar]
  45. Zhang, W.R., 1994. Bipolar fuzzy sets and relations: A computational framework for cognitive and modeling and multiagent decision analysis. In: Proceedings of the Fuzzy Information Processing Society Biannual Conference, San Antonio, TX, USA, pp. 305–309.
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