artificial neural networks

7/17/2019 Artificial Neural Networks

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Artifcial Neural Networks

- Introduction -

By Bhavik R Prajapati



History

• 1943: McCulloch–Pitts “neuron”

– Started the field

• 1962: Rosenblatts !erce!tron

– "earned its o#n #ei$ht %alues& con%er$ence !roof • 1969: Mins'( ) Pa!ert boo' on !erce!trons

– Pro%ed li*itations of sin$le+la(er !erce!tron net#or's

• 19,2: -o!field and con%er$ence in s(**etric net#or's

– .ntroduced ener$(+function conce!t

• 19,6: /ac'!ro!a$ation of errors – Method for trainin$ *ultila(er net#or's

• Present: Probabilistic inter!retations0 /a(esian and s!i'in$ net#or's



Basic concept o NeuralNetworks

• eural net#or' si*!lified *odel of biolo$ical neuron

s(ste*

• .ts !arallel distributed !rocessin$ s(ste* *ade u! of

hi$hl( interconnected neural co*!utin$ ele*ent thatha%e abilit( to learn b( acuire 'no#led$e and *a'e it

a%ailable for use

• arious learnin$ *echanics eit to enable acuire

'no#led$e i5e5learnin$ as R7..8 and abilit( sol%e !roble* usin$ acuired 'no#led$e as .RC

• /ase on learnin$ *echanis* classified into %arious t(!e



• s i*itations of central

ner%ous s(ste*

• -u*an brain ter*ed as

neurons #hich !erfor*

co*!utation such asco$nition0 lo$ical inference 0

!attern reco$nition etc

• -ence si*!lified i*itation of co*!utin$ b( neurons of brainhas been ter*ed as 7rtificial neural s(ste* or artificialneural net#or's



Human rain

• /rain contain about 1;<1; basic units

called as =R>S

• ach =R> turn connect to about

1;<4 other =R>S

• 7 euron is cell recei%e electro+che*

si$nal fro* its %arious source and turn

res!ond b( trans*ittin$ electricalsi$nal i*!ulse to other eurons

• he fastest neuron s#itchin$ ti*es are

'no#n to be on the order of 1;+3 sec5



Neuronsynapse



• 7 euron is co*!osed of nucleus+a cell bod( 'no#n as S>M7

• "on$ irre$ularl( sha!ed fila*ent attached to so*a are 'no#n

??R..S

• ??R..S act as in!ut channel 0all i@! fro* other neurons

arri%e throu$h the dendrites

• >ne so*a connected each other b( lin' is the 7A>

• 7A> a!!ear on out!ut cells are non linear threshold de%ice

!roduce %olta$e !ulse called SP.B or 7C.> P>.7"

• 7on ter*inates in a s!ecialied contact called SD7PS



• %er( co*!onent of the *odel bears direct analo$(

to the actual constituents of a biolo$ical neuron soter* as artificial neuron

• .ts *odel #hich for*s the basis of artificial eural

net#or'

!odel o Artifcial Neuron



● he choice of acti%ation function deter*ines the neuron *odel

Examples:

●hresholdin$ function:

●Si$nu* function:

●si$*oid function :

●-(!erbolic tan$ent function:



N"#RA$ N"%&'R(AR)HI%")%#R"

• Architecture inspire y structure ocerreral corte* o rain

• ANN defne as data processin+ system

consistin+ o lar+e no,o processin+elementartifcial neuron.

• ANN structure represented usin+

/IR")% 0RAPH• A +raph is ordered 1-tuple 23".

• 24vertices "4ed+es



• "ach "/0" is assi+ned 'RI"N%A%I'N

• /irect +raph also call as /I0RAPH



NN classifed ase on learnin+

!ethod

• 5in+le layer eed orward network

•

!ultilayer eed orward network

• Recurrent network



5in+le layer eed orward network



/oule layer eed orward network



Recurrent network



)haracteristic o Neural network



$earnin+ !ethod

• A/A$IN" Network

• !A/A$IN" Network

• Perceptron Network



A/A$IN" Network• A/A$IN" 4 Adaptive $inear Neural "lement

• %his network ramed y Bernard widrow ostanord uni,y usin+ 5upervised learnin+

• 'nly one output neuron and o6p value areipolar-7 or 87.



• 5upervised learnin+ al+orithm is same aspreceptron learnin+ al+orithm

• $east mean s9uare$!5. or /elta rule

• #sed in all HI0H 5P""/ !'/"!5 : %"$"PH'N" 5&I%)HIN0 5;5%"! to cancelecho in lon+ distance communication ckt



• !A/A$IN"4 !any A/A$IN" Network

• )ominin+ no, o A/A$IN" with manylayer span

!A/A$IN" Network



• $earnin+ rule adopted y !A/A$IN"network 4 !A/A$IN" adaptation

Rule !R .

• <orm o supervised learnin+ method

• 'jective 4 to adjust wei+ht such thaterror is minimum or current trainin+

pattern ut with little dama+e toprevious trainin+ pattern



• Its computational model o Ratina oeye so named as P"R)"P%R'N

• Rosenlatt +ive frst Perceptronmodel or ANN

• )ontain = units4

7,5"N5'R; #NI%

1,A55')IA%I'N#NI%

=,R"5P'N5"#NI%

P"R)"P%R'N Network



Architecture o ack propa+ation

network

• !ultilayer eed orward network withack propa+ation sometimes alsoknown as multilayer perceptron

• /etailed model o Back propa+ation

• 0enerali>ed learnin+ rule or ackpropa+ationdelta.



Architecture o ack propa+ation

network

• Rosenlatt?s perceptron was introduced andlimitation re+ardin+ to solution o linearityo inseparalenon linear separate. prolem

•

<or solvin+ linearly inseprale prolem• Re9uire one or more than perceptron

• "ach set up identiy y small linearityseparale section on input

• %hen cominin+ their output with eachother perceptron produced fnal output



solution



Non linear activationoperator



5in+le layer artifcial neural

Network



! lti l P t



!ulti layer Perceptron

artificial neural networks

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