B42010712

Mabrouka AL-Shebany et al. Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.07-12

RESEARCH ARTICLE

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OPEN ACCESS

Artificial Neural Network : A Brief Overview
Magdi Zakaria, Mabrouka AL-Shebany, Shahenda Sarhan
Sirte University SIRTE, LIBYA

Abstract
Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological
nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure
of the information processing system. Neural networks, have remarkable ability to derive meaning from
complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be
noticed by either humans or other computer techniques. A trained neural network can be thought of as an
"expert" in the category of information it has been given to analyze. This expert can then be used to provide
projections given new situations of interest and answer "what if" questions. so in this paper we tried to introduce
a brief overview of ANN to help researchers in their way throw ANN.

I.

Introduction

An Artificial Neural networks are very
powerful brain-inspired computational models.
Which have been employed in various areas such as
computing, medicine, engineering, economics, and
many others.An artificial neural network is based on
the optimization theory. An Artificial Neural
Network is a computational model inspired in the
functioning of the human brain. It is composed by a

set of artificial neurons (known as processing units)
that are interconnected with other neuron these
neurons depend on weights of the neural network. As
The word network in Neural Network refers to the
interconnection between neurons present in various
layers of a system. These weights represent the
connections between the neurons which determine
the impact of one neuron on another[1].

Fig. 1. Natural neuron
The first artificial neuron was firstly
proposed in a formal model in 1943 by McCulloch
and Pitts. They proved that this model of neuron was
able to perform any computable function using a
finite number of artificial neurons and synaptic
weights adjustable.
The neurons in the input layer receive the
data and transfer them to neurons in the first hidden
layer through the weighted links. Data are
mathematically processed and transferred the result
to the neurons in the next layer. The network’s output
is provided by the neurons in the last layer. The j-th
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neuron in a hidden layer processes the incoming data
(xi) by: (i) calculating the weighted sum and adding a
“bias” term (θj) according to:
𝑚
𝑛𝑒𝑡 𝑗 = 𝑖=1 𝑥 𝑖 ∗ 𝑤 𝑖𝑗 + 𝜃𝑗
𝑗 = (1, 2, 3, 𝑛)
[2]
neural networks are capable of adaptation to given
data; neural networks are capable of generalization
even when the input data set contains noise or
missing values (trained network has the capability of
correctly filling the value without affecting the
prediction); neural networks act as an universal
approximation for an arbitrary continuous function
with arbitrary precision.
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ANN can be defined based on the following three
characteristics:
1. The Architecture indicating the number of layers
and the no. of nodes in each of the layers.
2. The learning mechanism applied for updating the
weights of the connections.
3. The activation functions used in various layers
[3].

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 Neural network layers:
1. Dimensional layer (Single Layer Perceptron) is a
function of N real variables of the form
𝑁

𝑓 𝑥1 , … . . , 𝑥 𝑁 = 𝑠𝑔𝑛(

𝑤 𝑖 𝑥 𝑖 − 𝜃)
𝑖=1

Here xi are real variables, (x1, . . . , xN) takes
values in some domain U ⊂RN, wiare real
parameters(weights of the neuron), _ is the threshold
of activation of the neuron, the function sgm(x) = 1
for x ≥ 0 and is equal to zero for x < 0.

Fig. 2. An artificial neuron
We also consider the smoothed variant of
the above neuron for which instead of the function
sgm we use the smooth monotonous increasing
function sgm which varies from zero to unity. In
particular we consider the neuron of the form
𝑁

𝑓 𝑥1 , … . . , 𝑥 𝑁 = 𝑠𝑔𝑛

𝑤 𝑖 𝑥 𝑖 − 𝜃 , 𝑠𝑔𝑚 𝑥
𝑖=1

=

1
1 + 𝑒 −𝑥

2. Multi Layer Perceptron (MLP) : Multi-layer
consisting of three consecutive layers: an input, a
hidden, and an output layer. Every system is basically
a three layered system, which are Input layer, Hidden
Layer and Output Layer. The input layer has input
neurons which transfer data via synapses to the
hidden layer, and similarly the hidden layer transfers
this data to the output layer via more synapses. The
synapses stores values called weights which helps
them to manipulate the input and output to various
layers.

Fig.3. General structure of a neural network with two hidden layer

II.

Learning in ANN

There are three major learning paradigms;
supervised learning, unsupervised learning and
reinforcement learning. Usually they can be
employed by any given type of artificial neural
network architecture. Each learning paradigm has
many training algorithms.
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

Supervised learning
Supervised learning is a machine learning
technique that sets parameters of an artificial neural
network from training data. The task of the learning
artificial neural network is to set the value of its
parameters for any valid input value after having seen
output value. The training data consist of pairs of
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input and desired output values that are traditionally
represented in data vectors. Supervised learning can
also be referred as classification, where we have a
wide range of classifiers, each with its strengths and
weaknesses.
Choosing a suitable classifier (Multilayer
perceptron, Support Vector Machines, k-nearest
neighbor algorithm, Gaussian mixture model,
Gaussian, naive Bayes, decision tree, radial basis
function classifiers,…) for a given problem is
however still more an art than a science. In order to
solve a given problem of supervised learning various
steps has to be considered. In the first step we have to
determine the type of training examples[6][14]. In the
second step we need to gather a training data set that
satisfactory describe a given problem. In the third
step we need to describe gathered training data set in
form understandable to a chosen artificial neural
network. In the fourth step we do the learning and
after the learning we can test theperformance of
learned artificial neural network with the test
(validation) data set. Test data set consist of data that
has not been introduced to artificial neural network
while learning.
2.2 Unsupervised learning
Unsupervised learning is a machine learning
technique that sets parameters of an artificial neural
network based on given data and a cost function
which is to be minimized. Cost function can be any
function and it is determined by the task formulation.
Unsupervised learning is mostly used in applications
that fall within the domain of estimation problems
such as statistical modeling, compression, filtering,
blind source separation and clustering[14].In
unsupervised learning we seek to determine how the
data is organized. It differs from supervised learning
and reinforcement learning in that the artificial neural
network is given only unlabeled examples.
One common form of unsupervised learning
is clustering where we try to categorize data in
different clusters by their similarity. Among above
described artificial neural network models, the Selforganizing maps are the ones that the most
commonly use unsupervised learning algorithms.
2.3 Reinforcement learning
Reinforcement learning is a machine
learning technique that sets parameters of an artificial
neural network, where data is usually not given, but
generated by interactions with the environment.
Reinforcement learning is concerned with how an
artificial neural network ought to take actions in an
environment so as to maximize some notion of longterm reward. Reinforcement learning is frequently
used as a part of artificial neural network’s overall
learning algorithm.
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After return function that needs to be
maximized is defined, reinforcement learning uses
several algorithms to find the policy which produces
the maximum return. Naive brute force algorithm in
first step calculates return function for each possible
policy and chooses the policy with the largest return.
Obvious weakness of this algorithm is in case of
extremely large or even infinite number of possible
policies[1][14]. This weakness can be overcome by
value function approaches or direct policy
estimation. Value function approaches attempt to find
a policy that maximizes the return by maintaining a
set of estimates of expected returns for one policy;
usually either the current or the optimal estimates.
These methods converge to the correct estimates for a
fixed policy and can also be used to find the optimal
policy.
Similar as value function approaches the
direct policy estimation can also find the optimal
policy. It can find it by searching it directly in policy
space what greatly increases the computational cost.
Reinforcement learning is particularly suited to
problems which include a long-term versus shortterm reward trade-off. It has been applied
successfully to various problems, including robot
control, telecommunications, and games such as
chess and other sequential decision
making tasks.

III.

Neural Network Algorithms:



Radial basis functions network (RBF)
A radial
basis
function
network is
an artificial neural network that uses radial basis
functions as activation functions. The output of the
network is a linear combination of radial basis
functions of the inputs and neuron parameters.


Auto correlation function
Autocorrelation is the cross-correlation of
a signal with itself. It is the similarity between
observations as a function of the time interval
between them. It is a mathematical tool for finding
repeating patterns, such as the presence of a periodic
signal obscured by noise, or identifying the missing
fundamental frequency in a signal implied by
its harmonic frequencies. It is often used in signal
processing for analyzing functions or series of values,
such as time domain signals.


Self-organizing map (SOM) neural network
A self-organizing map (SOM) is a type
of artificial neural network (ANN) that is trained
using unsupervised learning to produce a lowdimensional (typically two-dimensional), discretized
representation of the input space of the training
samples, and called a map. Self-organizing maps are
different from other artificial neural networks in the
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sense that they use a neighborhood function to
preserve the topological properties of the input space.
The ideas of the SOFM combined with the elastic net
algorithm to solve Euclidean problems like the

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travelling salesman problem. A modified SOFM has
been used to solve broad classes of optimization
problems by freeing the technique from the Euclidean
plane [5].

Fig. 4. Architecture of a SOFM with nine neurons


Back propagation neural
Back propagation ANNs represented by
nonlinear networks. The back propagation (BP)
algorithmused for training multilayer networks by
means of error propagation via variational
calculations. It minimizes the sum of squared
approximation errors using a gradient descent
technique.When noisy training data are present, the
learned function can oscillate abruptly between data
points. This is clearly undesirable for function
approximation from noisy data [6].

IV.

Neural Network Applications:

Hop-field networks
Hop-field networks used to solve
optimization problems which are familiar to
theoperations researcher. although there is no
layerstructure to the architecture, and the weights are
constants and symmetric. Hop-field networks are a
fullyinterconnected system of N neurons. The
weights of the network store information about the
memories or stablestates of the network. Each neuron
has a state xi which is bounded between 0 and 1.
Neurons areupdated according to a differential
equation, and an energy function is minimized over
time because of the stable states of the network.
Hop-field and Tank showed that the weights
of a Hop-field network can be chosen so the process
of neurons minimizes the Hop-field energyfunction
and the optimization problem.
Each neuron i updates itself according to the
differential equation
dnet i
net i
=−
+
dt
τ

Image Indexing And Retrieval. SooBeom Park,
Jae Won Lee, Sang Kyoon Kim proposed a
content-based image classification method that
reflect the shape of an object based on pattern of
the texure feature[8].



Pattern Recognition : hierarchical neural
networks
improves
supervised
pattern
classification as it used in online back
propagation, improved records on MNIST, Latin
letters, Chinese characters, and traffic signs. Dan
Ciresan ,Ueli Meier, J¨urgenSchmidhuber
proposed a system to get data sets ranging from
handwritten digits (MNIST), handwritten
characters to 3D toys (NORB) and faces[9].



Space Exploration Robotic Vehicle And
Exploring The Land Of A Planet. It has the
capability to travel across the surface of a
landscape and other cosmic bodies. Artificial
neural networks have many advantages in space
applications due to its:
Generality
Performance
Adaptability
Low energy consumption

N

wij xj + Ii
j=1

xi=f(neti) Where f(.) is a sigmoidal output function
bounded by 0 and 1 and q is a constant [5].

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Classification : such as classification of brain
tumors based on many sources of information.
KailashD.Kharat&PradyumnaP.Kulkarni&M.B.
Nagori proposed method that can do
classification of brain tumors by the analysis of
Magnetic Resonance (MR) images and Magnetic
Resonance Spectroscopy (MRS) data of the
patients with benign and malignant tumors to
determine the type of tumors [7].












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







Robustness & Fault Tolerance: Youssef Bassil
proposes a path-planning solution for
autonomous robotic planetary rover systems
based on artificial neural network (ANN). The
proposed ANN uses a mix of activation
functions including Sigmoid for the hidden
neurons and linear for the output neurons [10].
Where Madhusmita Swain, Sanjit Kumar Dash,
Sweta Dash and AyeskantaMohapatra Proposed
The Multi-Layer Feed Forward Neural network
which is able to classify the three different types
of IRIS plants of 150 instances with just few
errors for the other one[3].
Wavelet Feature Extraction : proposed for image
classification problem. jay Kumar Singh, Shamik
Tiwari, V.P. Shukla proposed feature extraction
and classification of multiclass images by using
Haar wavelet transform and back propagation
neural network [11].
Multi-class object recognition is a critical
capability for an intelligence robot to perceive
its environment. YuhuaZheng and Yan Meng
Proposed a model combined a number of
modular neural networks to recognize multiple
classes of objects for a robotic system. The
population of the modular neural networks
depends on the class number of the objects to be
recognized and each modular network only
focuses on learning one object class. For each
modular neural network, both the bottom-up
(sensory-driven) and top-down (expectationdriven) pathways are attached together, and a
supervised learning algorithm is applied to
update corresponding weights of both pathways.
Also there are two different training strategies
are evaluated: positive-only training and
positive-and-negative training [12]
Pattern Classification WawanSetiawan and
Wiweka proposed a classifier model with neural
network approach based on the method used
Expectations Maximum (EM)[13].

V.

Conclusion

The computing world has a lot to gain from
neural networks. Their ability to learn by example
makes them very flexible and powerful. Furthermore
there is no need to devise an algorithm in order to
perform a specific task; i.e. there is no need to
understand the internal mechanisms of that task.
They are also very well suited for real time systems
because of their fast response and computational
times which are due to their parallel architecture.

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