16096 fuzzy applications

8/9/2019 16096 Fuzzy Applications

1/22

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'


2/22

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 #


3/22


4/22

' 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'


5/22


6/22

;' 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'


7/22

Fuzzy logic denotes a retreat $orm unrealisticre uirement o$ recision' ( 4@C6 DEF4@GH ) 3 IJKL MNOPQRS 3


8/22

-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=<


9/22

Fuzzy logic and 5tGs A lications

9ontents!0' 5ntroduction o$ Fuzzy Set theory


10/22

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


11/22

5ntroduction

+no@ledge Re resentatione"am le! age (Man Eld)

traditionalAge (Man t B=)

>= B= Ages

0

Membershi Function


12/22

5ntroduction

Fuzzy

Age (Man Eld)

>= B= Ages

0

Membershi Function

='2


13/22

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 =


14/22

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


18/22

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


19/22

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


20/22

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)


21/22

Ad%antages o$ $uzzy system modeling

0' The ability to model highly com le" business roblems'


22/22

16096 fuzzy applications

Documents