Information Technology Reference
In-Depth Information
r
i
·
inwhich the elements of the classical product of matrices (rows by columns)
1
i
n
r
i
,
a
ij
are substituted by max
1
i
n
T
0
(
a
ij
)
.
This composition is called the max-
T
0
product of matrices
, instead of the classical
sum-prod composition. Hence,
s
1
,...,
s
m
)
=[
μ
P
∗
]↗[
[
μ
Q
∗
]=
(
J
]
,
gives the CRI's output.
Example 3.4.5
With
μ
P
=
0
.
7
/
x
1
+
0
.
8
/
x
2
+
1
/
x
3
,μ
Q
=
0
.
9
/
y
1
+
0
.
6
/
y
2
+
0
.
8
/
y
4
,μ
P
∗
=
0
.
6
/
x
1
+
0
.
7
/
x
2
+
1
/
x
3
,
and
J
(
a
,
b
)
=
min
(
1
,
1
−
a
+
b
)
, follows:
a
11
=
J
(
0
.
7
,
0
.
9
)
=
min
(
1
,
1
−
0
.
7
+
0
.
9
)
=
1;
a
12
=
J
(
0
.
7
,
0
.
6
)
=
0
.
9;
a
13
=
J
(
0
.
7
,
0
)
=
0
.
3;
a
14
=
J
(
0
.
7
,
0
.
8
)
=
1
a
21
=
J
(
0
.
8
,
0
.
9
)
=
1;
a
22
=
J
(
0
.
8
,
0
.
6
)
=
0
.
8;
a
23
=
J
(
0
.
8
,
0
)
=
0
.
2;
a
24
=
J
(
0
.
8
,
0
.
8
)
=
1
a
31
=
J
(
1
,
0
.
9
)
=
0
.
9;
a
32
=
J
(
1
,
0
.
6
)
=
0
.
6;
a
33
=
J
(
1
,
0
)
=
0;
a
34
=
J
(
1
,
0
.
8
)
=
0
.
8
.
Hence,
⊛
⊞
10
.
90
.
31
⊝
⊠
=
(
[
μ
Q
∗
]=
(
0
.
60
.
71
)
↗
10
.
80
.
21
0
.
910
.
90
.
8
),
0
.
90
.
600
.
8
since:
(
max
(
W
(
0
.
6
,
1
),
W
(
0
.
7
,
1
),
W
(
1
,
0
.
9
)),
max
(
W
(
0
.
6
,
0
.
9
),
W
(
0
.
7
,
0
.
8
),
W
(
1
,
0
.
6
)),
max
(
W
(
0
.
6
,
0
.
3
),
W
(
0
.
7
,
0
.
2
),
W
(
1
,
0
)),
max
(
W
(
0
.
6
,
1
),
W
(
0
.
7
,
1
),
W
(
1
,
0
.
8
)))
=
(
max
(
0
.
6
,
0
.
7
,
0
.
9
),
max
(
1
,
1
,
0
.
6
),
max
(
0
.
9
,
0
.
9
,
0
),
(
.
,
.
,
.
))
=
(
.
.
.
)
max
0
6
0
7
0
8
0
910
90
8
. That is
μ
Q
∗
=
0
.
9
/
y
1
+
1
/
y
2
+
0
.
9
/
y
3
+
0
.
8
/
y
4
.
In the case
μ
P
is interpreted
P
=
more or less big
,
μ
Q
is interpreted
Q
=
not
is interpreted
P
∗
=
medium
, it is possible to agree on
Q
∗
very big
, and
μ
P
∗
=
more
or less big
.
3.4.2 Inference with Several Rules
Actually, there are no systems described by a single rule. What to do when a system
is described by, at least, two rules? With, for example
•
r1: If
x
is
P
1
, then
y
is
Q
1
•
r2: If
x
is
P
2
, then
y
is
Q
2
,
an input
μ
P
∗
gives
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