Marakas-Ch09

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Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Capter ! - "

Chapter 9:

Machines That Can Learn

Decision Support Systems in the

21st

Century , 2nd

Edition by George M. Marakas



Marakas: Decision Support Systes, 2nd Edition ! 2""#, $rentice%&a''

Chapter 9 % 2

9-1: Fuzzy Logic and

Linguistic Ambiguity(ur 'anguage is rep'ete )ith *ague and

iprecise concepts, and a''o)s +or

con*eyance o+ eaning through seanticapproiations.

These approiations are use+u' to huans,

but do not readi'y 'end these'*es to the ru'e%

based reasoning done on coputers.

-se o+ +uy 'ogic is ho) coputers hand'e

this abiguity.




Chapter 9 % #

The Basics of Fuzzy Logic

/n a 0pure1 'ogica' coparison, the resu't is

either +a'se "3 or true 43 and can be stored

in a binary +ashion.The resu'ts o+ a +uy 'ogic operation range

+ro " abso'ute'y +a'se3 to 4 abso'ute'y true3,

)ith stops in bet)een.

These operations uti'ie +unctions that assign

a degree o+ 0ebership1 in a set.




Chapter 9 % 5

A Simple Membeship Function !"ample

The #Tallness$ function ta%es a peson&s height and

con'ets it to a numeical scale fom ( to 1)*ee the statement #*e is Tall$ is absolutely false

fo heights belo+ , feet and absolutely tue foheights abo'e feet




Chapter 9 % 6

Fuzziness .esus /obability

There are soe subt'e di++erences:

$robabi'ity dea's )ith the 'ike'ihood thatsoething has a particu'ar property.

7uy 'ogic dea's )ith the degree to )hichthe property is present. 7or eap'e, aperson 8 +eet in height has a .6 degree o+ta''ness.




Chapter 9 % 8

Ad'antages and Limitations

of Fuzzy Logic Advantages: +uy 'ogic a''o)s +or the ode'ingand inc'usion o+ contradiction in a kno)'edgebase. /t a'so increases the syste autonoy

the ru'es in the kno)'edge base +unctionindependent o+ each other3.

Disadvantages: /n a high'y cop'e syste,use o+ +uy 'ogic ay becoe an obstac'e to

the *eri+ication o+ syste re'iabi'ity. 'so, +uyreasoning echaniss cannot 'earn +ro theiristakes.




Chapter 9 %

9-0: Atificial eual et+o%s

7irst proposed in 495"s as an attept to

siu'ate the huan brain;s cogniti*e 'earning

processes.

They ha*e abi'ity to ode' cop'e, yetpoor'y understood prob'es.

<<s are sip'e coputer%based progras

)hose +unction is to ode' a prob'e spacebased on tria' and error.




Chapter 9 % =

Leaning Fom !"peience

The process is:

4. piece o+ data is presented to a neura'net. The << 0guesses1 an output.

2. The prediction is copared )ith the actua'or correct *a'ue. /+ the guess )as correct,no action is taken.

#. n incorrect guess causes the << to

eaine itse'+ to deterine )hichparaeters to ad>ust.

5. nother piece o+ data is presented and theprocess is repeated.




Chapter 9 % 9

Fundamentals of eual 2omputing

The basic processing e'eent in the huanner*ous syste is the neuron. <et)orks o+these interconnected ce''s recei*e in+oration

+ro sensors in the eye, ear, etc./n+oration recei*ed by a neuron )i'' eitherecite it and it )i'' pass a essage a'ong thenet)ork3 or )i'' inhibit it suppressing

in+oration +'o)3.Sensiti*ity can change )ith passing o+ tie orgaining o+ eperience.




Chapter 9 % 4"

/utting a Bain in a Bo"

n << is coposed o+ three basic 'ayers:

4. The input 'ayer recei*es the data

2. The interna' or hidden 'ayer processes the data.

#. The output 'ayer re'ays the +ina' resu't o+ the net.




Chapter 9 % 44

3nside the euode

The neurode usua''y has u'tip'e inputs, each

input )ith its o)n )eight or iportance.

bias input can be used to ap'i+y the

output.The state +unction conso'idates the )eights o+

the *arious inputs into a sing'e *a'ue.

The trans+er +unction processes this state*a'ue and akes the output.




Chapter 9 % 42

Taining the Atificial eual et+o%




Chapter 9 % 4#

Sending the et to School:

Leaning /aadigms/n unsuper*ised 'earning paradigs, the <<

recei*es input data but not any +eedback about

desired resu'ts. /t de*e'ops c'usters o+ thetraining records based on data sii'arities.

/n a super*ised 'earning paradig, the <<

gets to copare its guess to +eedback

containing the desired resu'ts. The ostcoon o+ these is back propagation, )hich

does the coparison )ith s?uared errors.




Chapter 9 % 45

Benefits Associated +ith eual

2omputing *oidance o+ ep'icit prograing

@educed need +or eperts

<<s are adaptab'e to changed inputs

<o need +or re+ined kno)'edge base <<s are dynaic and ipro*e )ith use

b'e to process erroneous or incop'ete data

''o)s +or genera'iation +ro speci+ic in+o ''o)s inc'usion o+ coon sense into theprob'e%so'*ing doain




Chapter 9 % 46

Limitations Associated +ith

eual 2omputing <<s cannot 0ep'ain1 their in+erence

The 0b'ack bo1 nature akes accountabi'ity

and re'iabi'ity issues di++icu't

@epetiti*e training process is tie consuing

&igh'y ski''ed achine 'earning ana'ysts and

designers are sti'' a scare resource

<< techno'ogy pushes the 'iits o+ currenthard)are

<< re?uire 0+aith1 be iparted to the output




Chapter 9 % 48

9-4: 5enetic Algoithms and

5enetically !'ol'ed et+o%s/+ a prob'e has any so'ution, it suggests that

there is an optia' so'ution soe)here.

The +ie'd o+ anageent science has been

ab'e to tack'e increasing'y cop'e prob'esand +ind optia' so'utions.

This success 'eads us to tack'e prob'es

e*en ore cop'icated, creating a need +or

ore inno*ati*e so'ution ethods.

(ne such ethod is the genetic a'gorith.




Chapter 9 % 4

3ntoduction to 5enetic Algoithms

Like neura' nets, genetic a'goriths G3 are

based on bio'ogica' theory.

&ere, ho)e*er, Gs +ind their roots in the

e*o'utionary theories o+ natura' se'ection andadaptation.

The po)er o+ a G resu'ts +ro the ating o+

t)o popu'ation ebers to produce o++spring

that are soeties better than the parents.




Chapter 9 % 4=

Basic 2omponents of a

5enetic Algoithm

The sa''est units o+ in+oration are dubbed

genes, )hich cobine into chroosoes.

+ter a G is initia'ied, it uses a 0+itness

+unction1 to e*a'uate each chroosoe.The G then eperients by cobining the ost

+it chroosoes.

<et, the crosso*er phase sees these t)o 0good1chroosoes echange gene in+oration.

The utated chroosoes then >oin the poo'.




Chapter 9 % 49

Basic /ocess Flo+ of a

5enetic Algoithm




Chapter 9 % 2"

Benefits and Limitations Associated 6ith 5As

$opu'ation sie is a critica' +actor in the speed o++inding a so'ution, but at 'east it is re'ati*e'y easy topredict this speed.

Crosso*er and utation are interesting ideas, but

they shou'd not be used too +re?uent'y or toosparing'y, either3.

(ne ad*antage is that you are a')ays guaranteed tocoe up )ith at 'east a 0reasonab'e1 so'ution.

Ae can a'so app'y the to prob'es +or )hich )erea''y ha*e no c'ue on ho) to so'*e.

7ina''y, their po)er coes +ro sip'e concepts, not+ro a cop'icated a'gorithic procedure.




Chapter 9 % 24

9-7: Applications of Machines That Lean

<ippon Stee': b'ast +urnace contro' systethat uses <<s

Dai)a Securities and <EC: stock price chart

pattern recognitionMitsubishi E'ectric: neura' net and optica'scanning to recognie tet

<ippon (i': neura' net used +or diagnosis o+

pup *ibrationCredit scoring on 'oan app'ications, both toindi*idua's and corporations



Marakas: Decision Support Systes, 2nd Edition !2""# $ ti & ''

Chapter 9 % 22

The Futue of Machine Leaning

'ready, arti+icia' neura' nets eceed huan

capacity +or iso'ated instances.

Theoretica''y, a coputer can process datai''ion ties +aster than a huan.

7ortunate'y +or us, huans are so uch

better at ac?uiring data. Coputers >ust don;t

ha*e anything 'ike the +i*e senses.

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Marakas-Ch09