CHAPTER 2
LITERATURE REVIEW
2.1 Artificial Neural Network
Artificial neural network ANN, usually called Neural Network NN, is an algorithm that was originally motivated by the goal of having machines that
can mimic the brain. A neural network consists of an interconnected group of artificial neurons. They are physical cellular systems capable of obtaining,
storing information, and using experiential knowledge. Like human brain, the ANN’s knowledge comes from examples that they encounter. In human
neural system, learning process includes the modifications to the synaptic connections between the neurons. In a similar way, ANNs adjust their
structure based on output and input information that flows through the network during the learning phase.
Data processing procedure in any typical neural network has two major steps: the learning and application step. At the first step, a training database or
historical price data is needed to train the networks. This dataset includes an input vector and a known output vector. Each one of the inputs and outputs
are representing a node or neuron. In addition, there are one or more hidden layers. The objective of the learning phase is to adjust the weights of the
connections between different layers or nodes. After setting up the learning samples, in an iterative approach a sample will be fed into the network and
2.1 Artificial Neura ra
l l Network
Artifi fi
ci cial neural
l ne
e tw
tw ork A
A NN
NN ,
u u
su u
al l
ly ly
c cal
al le
led d
Neural Netwo work NN, is an
a algorithm
m th
th at was
a origina
a ll
ll y
y mo
mo ti
ti va
va te
te d
d by
by the goa
o l of
f h
h av
av ing ma
ch c
ines that can
n mi
mim mic
c the br
br ai
n. A neural network consists o f
an an inter
rco conn
nne ected gr
ou o
p of ar
ar ti
ifi ficial n
n eurons.
Th ey are phy
si cal cellular
s ys
te ms c
ap apable
l o
o f
f obtainin
ing, st
s orin
n g
information, and using e xp
er iential kn
ow ledge. Like
hu huma
a n
br br
ai ain
n, the e
AN N
N’s kn
ow ledge come
s fr
om e
xamp le
s that they
en co
un te
er. In n
hu hu
man n
ne e
ur al system,
lea rn
in g
pr oc
es s
in cl
ud es t
he mod
if ic
ations to t
the synap ptic
c co
o nn
ection s
betwee n
the neurons. In
a similar way, ANNs a adju
u st
st the ir
ir structur
e ba
base se
d d
on on
o o
ut ut
pu pu
t t
an and inpu
pu t
t in in
fo fo
rm rm
at at
io io
n n
th th
at at
flo ws through the
he network during the learning phase.
e Da
Data ta
p p
ro ro
ce ce
ss ss
in ing
g pr
pr oc
oc ed
ed ur
ur e
e in in
a a
ny ny typ
yp ic
ical al n
n eu
eu ra
ra l
l ne
ne tw
tw or
or k
k ha
ha s
s tw
two o
ma majo
jor r st
steps: th
th e
le le
ar ar
ni ni
n ng a
a nd
nd application n
step. At t
t the first step
ep ,
, a
tr tr
ai ai
ni ni
ng ng d
d at
atabase or historical price data is neede
e d
to train t
t he networks. This dataset includes an
input vector and a known ou utput vect
o or. Each one of the inputs and outputs
are representing a node or neu u
ro r
n. I I
n n addition, there are one or more hidden
layers. The objective of the learn rn
ing phase is to adjust the weights of the
the resulting outputs will be compared with the known outputs. If the result and the unknown output are not equal, changing the weights of the
connections will be continued until the difference is minimized. After acquiring the desired convergence for the networks in the learning process,
the validation dataset is applied to the network for the validating step Shahkarami A. et al. 2014.
Fig. 1 An artificial neural network is an interconnected group of nodes. Source : SPE International, Colorado, USA, 16–18April 2014.
2.2 Review of previous researches
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