Civil Engineering Reference
In-Depth Information
Tabl e 2
Fuzzy opinions of the respondents
Your satisfaction with
the ruling party's
1 = Very
5 = Very
2005 policy
dissatisfied
2 = Dissatisfied
3 = Common
4 = Satisfied
satisfied
x
1
0
0
0
0
1
x
2
1
0
0
0
0
x
3
0
0
1
0
0
x
4
0
0.5
0
0.5
0
x
5
0
0
0.5
0
0.5
x
6
0
0
0.8
0
0.2
Tabl e 3
Fuzzy respondents
views distances
x
1
x
2
x
3
x
4
x
5
x
6
x
1
0
1.00
0.50
0.44
0.19
0.36
x
2
0
0.50
0.56
0.81
0.64
x
3
0
0.06
0.31
0.14
x
4
0
0.25
0.08
x
5
0
0.17
x
6
0
Definition 3.2.
The distance between two discrete fuzzy samples. Set
U
as a
domain,
L
=
L
1
=
1
,
L
2
=
2
,...,
L
k
=
k
as a set of k-linguistic ordered variables
distributed in the domain. If
x
i
=
m
i
1
/
L
1
+
m
i
2
/
L
2
+
...
+
m
ik
/
L
k
,
i
=
1
,
2
,...,
n
and
k
j
1 are the two fuzzy samples drawn from the domain, the denominator
k is the range of the ordered linguistic variables. Then the distance between two
discrete fuzzy samples is defined as
1
m
ij
=
∑
=
j
1
m
ij
|
L
j
−
c
j
|
k
j
=
1
m
ij
L
j
c
i
+
∑
=
dfi
=
,
c
i
=
k
−
1
k
j
k
j
1
m
ij
|
L
j
−
c
j
|
1
m
ij
|
L
j
−
c
j
|
1
c
i
+
∑
c
i
+
∑
=
=
d
(
df
1
,
df
2
)=
1
|
−
|
k
−
k
−
1
k
−
1
From the definition, the maximum distance d is 1, and the minimum is 0. The
smaller the d value is, the more approximate the two fuzzy samples; the higher the
d value is, the less approximate the two fuzzy samples.
Example 3.2.
The fuzzy magnitude of the relative distance can be seen from each
sample's differences in the preferences of different linguistic variables, as shown in
Tab les
2
and
3
.
4
Empirical Studies and Findings
The analysis of the descriptive statistics and sample structure of the students'
conditions of Internet usage is as shown in Table
4
.
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