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October 2007 Volume 10 Number 4 - Educational Technology ...

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we can see that<br />

S( X ) − S(<br />

Y ) S(<br />

Y ) − S(<br />

X )<br />

=<br />

,<br />

2<br />

2<br />

S( X ) − S(<br />

Y ) S(<br />

Y ) − S(<br />

Y )<br />

1−<br />

= 1−<br />

2<br />

2<br />

and M(X, Y) = M(Y, X). Q. E. D.<br />

Let à and B ~ be two vague sets in the universe of discourse U, U = {u1, u2, …, un}, where<br />

à = [ t ~ ( u ), 1−<br />

f ~ ( u )] / u1 + [ t ( ), 1 ( )]<br />

A 1 A 1<br />

~ u − f 2 ~ u / u2 + … + [ t ( ), 1 ( )]<br />

A<br />

A 2<br />

~ un<br />

− f ~ u<br />

A<br />

A<br />

n / un,<br />

and<br />

B ~ = t ( u ), 1−<br />

f ( u )] / u1 + t ( u ), 1−<br />

f ( u )] / u2 + … + t ( u ), 1−<br />

f ( u )] / un.<br />

[ ~<br />

B 1 ~<br />

B<br />

1<br />

[ ~ 2 ~<br />

B<br />

B<br />

2<br />

[ ~<br />

B<br />

n ~ n<br />

B<br />

Let ~ ( i)<br />

A u V = [ ) ( ~<br />

A i u t , 1 – ) ( ~<br />

A i u f ] be the vague membership value of ui in the vague set Ã, and let ~ ( )<br />

B i u V =<br />

[ ~ ( )<br />

B i u t , 1 – ) ( ~<br />

B<br />

i u f ] be the vague membership value of ui in the vague set B ~ . By applying Eq. (5), we can see<br />

that the score ( ~ ( ))<br />

A i u V S of the vague membership value ) ( ~<br />

A i u V can be evaluated as follows:<br />

( ~ ( ))<br />

A i u V S = ) ( ~<br />

A i u t – ) ( ~<br />

A i u f ,<br />

and the score ( )) V S of the vague membership value ) ( V can be evaluated as follows:<br />

( ~<br />

B i u<br />

( )) V S = ) ( t – ), ( f<br />

( ~<br />

B i u<br />

~<br />

B i u<br />

~<br />

B i u<br />

~<br />

B i u<br />

where 1 ≤ i ≤ n. Then, the degree of similarity H(Ã, B ~ ) between the vague sets à and B ~ can be evaluated by the<br />

function H,<br />

n ~ ~ 1<br />

H ( A,<br />

B)<br />

= ∑ M ( V ~ ( u ), V u i ~ ( ))<br />

B i<br />

n i=<br />

1<br />

A<br />

1 ( ~ ( )) ( ~ ( ))<br />

∑ 1<br />

,<br />

= 1<br />

2 ⎟ ⎟<br />

n ⎛ S V u − S V u ⎞<br />

= ⎜ A i B i<br />

−<br />

(7)<br />

n i ⎜<br />

⎝<br />

⎠<br />

where H(Ã, B)∈[0, 1]. The larger the value of H(Ã, B ~ ), the higher the similarity between the vague sets à and B ~ .<br />

Let à and B ~ be two vague sets in the universe of discourse U, U = {u1, u2, …, un}, where<br />

à = [ t ~ ( u ), 1−<br />

f ~ ( u )] / u1 + [ t ( ), 1 ( )]<br />

A 1 A 1<br />

~ u − f 2 ~ u / u2 + … + [ t ( ), 1 ( )]<br />

A<br />

A 2<br />

~ un<br />

− f ~ u<br />

A<br />

A<br />

n / un,<br />

and<br />

B ~ = [ t ~ ( u ), 1−<br />

f ~ ( u )] / u1 + [ t ( ), 1 ( )]<br />

B 1<br />

B<br />

1<br />

~ u − f 2 ~ u / u2 + … + [ ( ), 1 ( )]<br />

B<br />

B<br />

2<br />

~ un<br />

f<br />

B<br />

~ un<br />

B<br />

The proposed similarity measure between vague sets has the following properties:<br />

Property 3: Two vague sets à and B ~ are identical if and only if H(Ã, B ~ ) = 1.<br />

Proof:<br />

(i) If à and B ~ are identical, then<br />

t − / un.<br />

228

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