Materi 7 Artificial Neural Networks
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Transcript of Materi 7 Artificial Neural Networks
7/21/2019 Materi 7 Artificial Neural Networks
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ArtifcialNeural
Networks
Yufs Azhar Yufs Azhar
Teknik Inormatika - UMM Teknik Inormatika - UMM
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Latar Belakang
• Kemamuan manusia !alam memrosesinormasi" mengenal wa#ah" tulisan" !s$%
• Kemamuan manusia !alam mengi!entifkasiwa#ah !ari su!ut an!ang &ang $elum ernah
!ialami se$elumn&a%
• Bahkan anak-anak !aat melakukan hal ts$%
• Kemamuan melakukan engenalan meskiun
ti!ak tahu algoritma &ang !igunakan%• 'roses engenalan melalui enin!eraan
$erusat a!a otak sehingga menarik untukmengka#i struktur otak manusia
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Latar $elakang
• (ierca&ai $ahwakekuatan komutasiotak terletak a!a
– hu$ungan antar sel-sel s&ara
– hierarchicalorganization
– fring characteristics
– $an&akn&a #umlahhu$ungan
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)truktur *aringan a!a +tak
• Neuron a!alah satuan unit emroses terkecil a!a otak
• Bentuk stan!ar! ini mungkin !ikemu!ian hari akan $eru$ah
• *aringan otak manusia tersusun ti!ak kurang !ari ,,. $uahneuron &ang masing-masing terhu$ung oleh sekitar ,,/ $uahdendrite
• 0ungsi !en!rite a!alah se$agai en&amai sin&al !ari neuronterse$ut ke neuron &ang terhu$ung !engann&a
• )e$agai keluaran" setia neuron memiliki axon, se!angkan$agian enerima sin&al !ise$ut synapse
• )e$uah neuron memiliki ,-,% s&nase
• 'en#elasan le$ih rinci tentang hal ini !aat !ieroleh a!a !isilinilmu biology molecular
• )ecara umum #aringan sara ter$entuk !ari #utaan 1$ahkan le$ih2struktur !asar neuron &ang terinterkoneksi !an terintegrasiantara satu !engan &ang lain sehingga !aat melaksanakanaktiftas secara teratur !an terus menerus sesuai !enganke$utuhan
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Synapse
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)e#arah
• Mc3ulloch 4 'itts 1,56.2 !ikenal se$agaiorang &ang ertama kali memo!elkan NeuralNetwork% )amai sekarang i!e-i!en&a masihteta !igunakan" misaln&a7 – $ertemuan&a $e$eraa unit inut akan
mem$erikan comutational ower
– A!an&a threshol!
• 8e$$ 1,5652 mengem$angkan ertama kali
learning rule 1!engan alasan $ahwa #ika 9neurons akti a!a saat &ang $ersamaanmaka kekuatan antar mereka akan$ertam$ah2
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)e#arah
• Antara tahun ,5/-,5:an $e$eraaeneliti melangkah sukses a!aengamatan tentang ercetron
• Mulai tahun ,5:5 meruakan tahunkematian a!a enelitian seutar NeuralNetworks hamir selama ,/ tahun 1Minsk&
4 'aert2• Baru a!a ertengahan tahun ;-an
1'arker 4 Le3un2 men&egarkan kem$alii!e-i!e tentang Neural Networks
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A Neuron
© 2000 John Wiley & Sons, Inc.
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Konse (asar 'emo!elanNeural Networks
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• )e#umlah sin&al masukan x !ikalikan !engan
masing-masing enim$ang &ang $ersesuaian W • Kemu!ian !ilakukan en#umlahan !ari seluruh
hasil erkalian terse$ut !an keluaran &ang!ihasilkan !ilalukan ke!alam ungsi engaktiuntuk men!aatkan tingkatan !era#a! sin&alkeluarann&a F(x.W)
• <alauun masih #auh !ari semurna" namunkiner#a !ari tiruan neuron ini i!entik !engankiner#a !ari sel otak &ang kita kenal saat ini
• Misalkan a!a n $uah sin&al masukan !an n $uahenim$ang" ungsi keluaran !ari neuron a!alahseerti ersamaan $erikut7
01="<2 > 1w,=, ? @ ?wn=n2
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0ungsi-ungsi aktiasi
• Stept(x) = 1 if x >= t, else 0• Sign(x) = +1 if x >= 0, else –1
• Sigmoid(x) = 1/(1+e-x)
• Identity Function
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The frst Neural Networks
X1 X2 Y
0 0 00 1 0
1 0 0
1 1 1
X1
X2
Y
1
1 Threshold=2
Funsi !"#
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The frst Neural Networks
X1 X2 Y
0 0 00 1 1
1 0 1
1 1 1
X1
X2
Y
2
2 Threshold=2
Funsi $%
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The frst Neural Networks
X1 X2 Y
0 0 00 1 0
1 0 1
1 1 0
X1
X2
Y
2
1 Threshold=2
Funsi !"#"$T
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'ercetron
• )inonim untuk)ingle-La&er"
0ee!-0orwar!Network
• (iela#ariertama kali
a!a tahun/-an
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<hat can ercetronsreresent
0,0
0,1
1,0
1,1
0,0
0,1
1,0
1,1
ANDXOR
• Fungsi yang memisahkan daerah menjadi seperti diatas
dikenal dengan Linearly Separable
• Hanya linearly Separable functions yang dapat
direpresentasikan oleh suatu perceptron
h
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<hat can ercetronsreresent
inear Separability is also possible in more than ! dimensions "
but it is harder to #isualise
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3ase stu!& - AN(
X1
X2
Y
W1
Threshold=2
Funsi !"# den'n (i's
1
W2
W)
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(escrition o arameter
i!s x1 x" #
-,
-, , -, ,
-, , , ,
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Training a ercetron
t $ %
y
&
'(
)( $ *
)! $ *
)% $ *
*rror+-d'e W
$u-u = 1 i/ $u-u = 2
$u-u = 0 i/ $u-u 2
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8ow to u!ate W
3 4 le'rnin r'e 4 're
$ 4 ou-u
Xi 4 (il'n'n in-u 5e I y'n error
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salah
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salah
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salah
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salah
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salah
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salah
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#'n seerusny' s'6-'i (o(o y'n dih'sil5'n coco5 unu5 se6u' d'' 7
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Ceerensi
• Intro!uction to AI7 Neural Networks" DrahamKen!all%
• Intro!uction to Neural Networks" Cocha%
• 'engenalan ola $er$asis Neural Networks" Bu!iCahar!#o" *urusan Teknik Elektro" ITB%
• Konse !asar *aringan )&ara Tiruan !anemo!elann&a" Ci&anto )igit" 'oliteknik ElektronikaNegeri )ura$a&a" *uli 96%
• Notes on Neural Networks" 'ro% Ta!aki" MachineLearning counterart meeting" 'oliteknikElektronika Negeri )ura$a&a" +kto$er 9/%