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The PB model is a particular case of TS systems, as it is derived from a set of
fuzzy if-then rules with singleton consequents such that
IF x is W ˃˄ ,
THEN
x is f
˙
(˃, ˄ )
2 is a state vector, W ˃˄ = (w 1 (
x 1 ), w 2 (
T
which in a two-dimensional case, x
∈ R
x 2 ))
T
is a membership function vector, f
(˃, ˄ ) = (
f 1 (˃, ˄ ),
f 2 (˃, ˄ ))
R is a singleton
consequent vector, and
˃, ˄
Z are integers (1
˃
n 1 ,
1
˄
n 2 ) defined by,
˃(
x 1 ) =
d 1 (
max
(
i
))
where d 1 (
i
)
x 1 ,
˄ (
x 2 ) =
d 2 (
max
(
j
))
where d 2 (
j
)
x 2 .
R ˃˄ , the PB models that approximates a general nonlinear function is
expressed as,
For x
˃ +
i = ˃
1
˄ +
j = ˄ w
1
j
2 (
i
f 1 (
x 1 ,
x 2 ) =
1 (
x 1 )w
x 2 )
f 1 (
i
,
j
),
˃ +
i = ˃
1
˄ +
j = ˄ w
1
j
2 (
i
f 2 (
x 1 ,
x 2 ) =
1 (
x 1 )w
x 2 )
f 2 (
i
,
j
),
˃ +
i = ˃
1
˄ +
j = ˄ w
1
j
2 (
i 1 (
g(
x 1 ,
x 2 ) =
x 1 )w
x 2 )g(
i
,
j
),
˃ +
i = ˃
1
˄ +
j = ˄ w
1
j
2 (
i
h
(
x 1 ,
x 2 ) =
1 (
x 1 )w
x 2 )
h
(
i
,
j
),
where
w 1 (
x 1 )
=
1
ʱ,
w ˃ + 1
1
(
x 1 ) = ʱ,
w 2 (
x 2 )
=
1
ʲ,
w ˄ + 1
2
(
x 2 ) = ʲ,
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