16096 fuzzy applications
TRANSCRIPT
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Fuzzy Sets Fuzzy Arithmetic Fuzzy Relations
Fuzzy Logic Fuzzy Measure (Possibility Theory) Design Process and Design Tools A lications! e" ert systems# $uzzy controllers#
attern recognition# databases and in$ormationretrie%al# decision ma&ing'
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Te"tboo&! Fuzzy Sets and Fuzzy Logic# Theory andA lications eorge *' +lir , -o .uan# Prentice/all# 0112'
Re$' 3
Fuzzy sets# 4ncertainty# and 5n$ormation# ' *' +lir andTina A' Floger# Prentice /all# 0166' 3 Fuzzy Set Theory and 5ts A lications# /' 7*'
8immermann# 0110' 3 Fuzzy Logic! 5ntelligence# 9ontrol# and 5n$ormation#
*ohn .en# Reza Langari# Prentice /all# 0111' 3 Fuzzy :ngineering# -art +os&o# Prentice /all# 011;' 3 #
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' Fuzziness is one $eature o$ natural language sodoes not necessarily im ly the loss o$
meaning$ul semantics'2' A lication roadma o$ in$ormation technology!
numerical analysis# large database# &no@ledgemanagement' So# @e must $irst &no@ thecharacteristics o$ the @orld and its &no@ledge#then e" lore the ossibility and limitation o$&no@ledge'
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;' Traditional A5 aradigms! $irst order logic (*ohnMc9arthy# Cilsson +o@als&i) ad7hoc techni uesand heuristic rocedures' (Mar%in Mins&y (M5T)#Roger Schan&)' L' 8adeh! using $uzzy logic(a ro"imate reasoning# non7discrete) instead o$$irst order logic as the basis o$ A5 in commonsense reasoning'
8. !"#$%&'( ) *+,!"-./0$1 fuzzy knowledge( $ discrete ) 2common-sense reasoning ,34 5%
1' La@ o$ 5ncom atibility! As com le"ity rises# recise statements lose meaning and meaning$ulstatements lose recision'
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Fuzzy logic denotes a retreat $orm unrealisticre uirement o$ recision' ( 4@C6 DEF4@GH ) 3 IJKL MNOPQRS 3
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-remetmann limit! Co data rocessing system#@hether arti$icial or li%ing# can rocess more than
bits er second er gram o$ its mass' ( uantumtheory) transcom utational roblems
/o@ to deal @ith systems and associated roblems@hose com le"ities are beyond our in$ormation
rocessing limits
A;0=<
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Fuzzy logic and 5tGs A lications
9ontents!0' 5ntroduction o$ Fuzzy Set theory
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5ntroduction
01B2 Fuzzy Set (Pro$' Lot$i A'8adeh#49-)01BB Fuzzy logic (Dr' Peter C'Marinos# -ell
Lab)
01;< Fuzzy Measure (Pro$'Michio Sugeno)
Fuzzy Set
Fuzzy :%ent
9ris:lement
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5ntroduction
+no@ledge Re resentatione"am le! age (Man Eld)
traditionalAge (Man t B=)
>= B= Ages
0
Membershi Function
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5ntroduction
Fuzzy
Age (Man Eld)
>= B= Ages
0
Membershi Function
='2
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Fuzzy Logic
A subset fuzzythein xelement theof membership x A !)(
E set referencetheof element an x !
E of subset Fuzzy B A ##
H0#=I#)#()#( == ba xb xa B A
)#( ba MIN ba =
)#( ba MAX ba =
aa = 0
)()( bababa =
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A lication 9ontrol la@s o$ a ?ashing Machine
Laundry %olume (J)
Lo@ Mid /igh
$abric
uality(K)
So$tS N ?ea& S N ?ea& S N STDT N Short T N Short T N STD
More or lessso$t
S N ?ea& S N STD S N STDT N Short T N STD T N STD
More or less/ard
S N ?ea& S N STD S N StrongT N Short T N STD T N Long
/ard S N ?ea& S N STD S N StrongT N Short T N STD T N Long
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A lication >Fuzzy Automatic ?ashing Machine
F488.9ECTREL
Laundry%olume
(J)
/ighMidLo@
$abricuality
(K)
/ardMidSo$t
Stream strength N ?ea& ?ashing time N Short
Stream strength N Strong?ashing time N Long
Stream strength N Strong?ashing time N Short
(E timal ?ashing 9ycle)
Stream strength
?ashing time
laundry %olume
o timum@ater le%el
$abricuality
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A lication >Fuzzy7Ceuro ?ashing Machine(Panasonic)
F488.
5CF:R:C9:
C:4RAL C:T
Tuningmembershi$unctions
?ater Le%elKuantity(5CP4T) (E4TP4T)
Turbidity(E tical sensor)
9hange RateE$ Turbidity
?ater Stream Strength
?ashing 9ycle Time
Rinse 9ycle TimeDrain 9ycle Time
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A lication >Fuzzy7Ceuro ?ashing Machine(/itachi Sanyo)
F488. 5CF:R:C9:
C:4RAL
C:T
?ater Stream StrengthKuality( )(5CP4T)
(E4TP4T)
Kuantity(>)
?ashing 9ycle Time
Rinse 9ycle Time
Drain 9ycle Time
9EMP:CSAT5EC
Kuality( )
Kuantity(>)
9onducti%itySensor(2)(Room Tem (6) 3 Sanyo)
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Ad%antages o$ $uzzy system modeling
0' The ability to model highly com le" business roblems'
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