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Adaptive Resonance Theory (ART)

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26-02-2024
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2022/2023

The document is a comprehensive guide on Adaptive Resonance Theory (ART) and its application in neural networks and machine learning. It details the principles of ART, explaining how it enables neural networks to remain stable and plastic in response to new information. Key concepts such as the winner-takes-all mechanism, neuron output calculation, weight updates, and the importance of supervised learning for binary classification are discussed.

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Y 7
0.7 toy
0.8 5.4

94
0.3 0.2
V h
x 203 x
X 712 0


Process of ART Iwinnertakes all ansupervise

6 compare V winner
4 1 loser 2 0

V EX W
Tool to check value P as

if P update the weight


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IX w
Ex
Wb
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y
Inti 0.5
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EYE

its p create new outputneuron

New Wbi Xi
neuron i
w
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, Y 2
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it
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inital condition
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o

72 O
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P 0.5 94
T L
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X I 72 0


O compare V winner 4 1 loser 2 0
Y I 72 0
V EX W
Y Yz

of 5.4 07 Is

y
V2 1 0.7 0 0.5 0.7
U S V2 X X
V 0.8 7 Ve o
Y is thewining Neuron with output Yi 1 72 0


From Y
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Y



94
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0.4 C p 0.51

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Document information

Uploaded on
February 26, 2024
Number of pages
8
Written in
2022/2023
Type
CASE
Professor(s)
Hani hagras
Grade
A+

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