Study of Artificial Neural Network
Sunakshi Jain#1, Yatika Goel#2 , Deepali Singhal#3
#1,2
Computer Science & Engineering Department, R.K.G.I.T.W.
U.P. Technical University, U.P.
1swtsunakshi19@gmail.com
2yatikag92@gmail.com
3deepalisinghal@rkgitw.edu.in
Abstract— In this paper, we are elaborating Artificial Neural
Network or ANN, its various characteristics and business
applications. In this paper we also show that “what are neural
networks” and “Why they are so important in today’s Artificial
intelligence?” ANN provides a very exciting alternatives and
other application which can play important role in today’s
computer science field. The concept of ANN is basically
introduced from the subject of biology where neural network
plays a important and key role in human body. In human body
work is done with the help of neural network. There are some
Limitations also which are mentioned.
Keywords— Artificial Neural Network, Artificial Neuron,
Characteristics, Applications, Limitations.
3.
channels, all inputs from other neurons arrive
through the dendrites.
Axon- It also attached to the soma and is electrically
active and serves as an output channel.
The structural constituents of a human brain termed Neurons
are the entities, which perform computations such as
cognition, logical inference, and pattern recognition and so on.
Hence the technology, which has been built on a simplified
imitation of computing by neurons of a brain, has been termed
Artificial Neural Systems (ANS) technology or Artificial
Neural Network (ANN).
I. Neural network architecture
I. Introduction
Work on artificial neural networks commonly referred to as
“neural networks”, has been motivated right from its inception
by the recognition that the human brain computers in an
entirely different way from the conventional digital
computers. The human brain is one of the most complicated
things .Brain contains about 1010 basic units called Neurons. A
neuron is a small cell that receives electro-chemical signals
from its various sources and in turn responds by transmitting
electrical impulses to other neurons.
Despite their different activities, all neurons share common
characteristics:1. Soma – A neuron is composed of a nucleus-a cell
body known as soma.
2. Dendrite – Attached to the soma are long irregularly
shaped filaments called dendrites. It behaves as input
An Artificial Neural Network is defined as a data processing
system consisting of a large number of simply highly
interconnected processing elements in an architecture inspired
by the structure of the cerebral cortex of the brain. There are
several classes of NN, classified according to their learning
mechanisms. However, we identify three fundamentally
different classes of Networks.
Similar to biological Neuron Artificial Neural Network also
have neurons which are artificial and they also receive inputs
from the other elements or other artificial neurons and then
after the inputs are weighted and added, the result is then
transformed by a transfer function into the output. The transfer
function may be anything like Sigmoid, hyperbolic tangent
functions or a step.
Even though it has its own limitations, it is applied to a wide a
range of practical problems and has successfully demonstrated
its power.
III. Characteristics of artificial neural network
Conventionally, a computer operates through sequential linear
processing technologies. They apply formulas, decision rules,
and algorithms instructed by users to produce outputs from the
inputs. Conventional computers are good at numerical
computation. But ANNs improve their own rules; the more
decisions they make, the better the decisions may become.
There are six main characteristics of ANN technology Network Structure-The Network Structure of ANN
should be simple and easy. There are basically two
types of structures recurrent and non recurrent
structure. The Recurrent Structure is also known as
Auto associative or Feedback Network and the Non
Recurrent Structure is also known as Associative or
feed forward Network.
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Parallel Processing Ability- Parallel Processing is
done by the human body in human neurons are very
complex but by applying basic and simple parallel
processing techniques we implement it in ANN like
Matrix and some matrix calculations.
Distributed Memory-ANN is very huge system so
single place memory or centralized memory cannot
fulfil the need of ANN system so in this condition we
need to store information in weight matrix which is
form of long term memory because information is
stored as patterns throughout the network structure.
Fault Tolerance Ability-ANN is a very complex
system so it is necessary that it should be a fault
tolerant. Because if any part becomes fail it will not
affect the system .
Learning Ability-In ANN most of the learning rules
are used to develop models of processes, while
adopting the network to the changing environment
and discovering useful knowledge.
IV. BACKPROPAGATION NETWORKS
Back propagation is a systematic method of training
multilayer artificial neural networks. It is built on high
mathematical foundation and has very good application
potential.
A. Single layer artificial neural network
Consider a single layer feed forward neural network
consisting of an input layer to receive the inputs and an output
layer to output the vectors respectively. The input layer
consists of ‘n’ neurons and the output layer consists of ’m’
neurons. Indicate the weight of the synapse connecting ith
input neuron to the jth output neuron as wij . The inputs of the
input layer and the corresponding outputs of the output layer
are given as Assume, we use linear transfer function for the
neurons in the input layer and the unipolar sigmoidal function
for the neurons in the output layer
OI  = I I  (linear transfer function)
nx1
mx1
IOj =W1jII1 + W2jII2 +…………….+WnjIIN
Hence , the input to the output layer can be given as
O IO  = [W]T OI  = [W]T I I 
mx1
mxn
nx1
Using the unipolar sigmoidal or squashed-S function and the
slope of this function for neurons in the output layer, the
output is given by
OOk = 1/(1 + e-λIOk)
OO  = f[WI]
B. Model for multilayer perceptron
The adapted perceptrons are arranged in a layers and so the
model is termed as multilayer perceptron. This model has
three layers: an input layer, and output layer, and a layer in
between not connected directly to the input or the output and
hence, called the hidden layer. For the perceptrons in the input
layer, we use linear transfer function, and for the perceptrons
in the hidden layer and the output layer, we use sigmoidal or
squashed-S functions. The input-output mapping of multilayer
perceptron is represented by
O=N3[N2[N1[I]]]
N1, N2, and N3 represent nonlinear mapping provided by
input, hidden and output layers respectively. Multilayer
perceptron provides no increase in computational power over
a single layer neural network unless there is a nonlinear
activation function between layers.
V. Applications of artificial neural network
There are many applications of ANNs in today's business.
Financial institutions are improving their decision making by
enhancing the interpretation of behavioral scoring systems and
developing superior ANN models of credit card risk and
bankruptcy. Two actual ANN applications are now described.
A. Airline Security Control
With the increasing threat of terrorism, airline passengers'
bags in international airports such as New York and London
go through an unusually rigorous inspection before being
loaded into the cargo bay. In addition to using metal detector
and x-ray station to detect metal weapons, these airports use
ANNs to screen for plastic explosives. They use a detection
system which bombards the luggage with neutrons and
monitors the gamma rays that are emitted in response. The
network then analyzes the signal to decide whether the
response predicts an explosive. Explosive materials are rich
in nitrogen, but so are some benign substances, including
protein-rich materials, such as wool and leather. To minimize
the classification error, supervised training was conducted.
The ANN was fed with a batch of instrument reading as well
as the information on whether explosives were indeed present.
The trained network was able to achieve its intended purpose.
The reduction in false alarms translates into many less bags
that must be opened and examined each day. In turn, it
reduces the cost of airport operations, increases the efficiency
of the check-in process, and improves the satisfaction of
Customers.
B. Investment Management and Risk Control
Neural Systems makes use of a supervised network to mimic
the recommendations of money managers on the optimal
allocation of assets among Treasury instruments. The
application demonstrated how well an ANN can be trained to
recognize the shape and evolution of the interest yield curve.
The network was trained on measured and calculated
economic indicators, such as the evolution of interest rates,
price changes, and the shape and speed of the change of the
yields curves. The network could then determine the optimal
allocation among segments in various Treasury instruments
being measured against a benchmark or comparator
performance index. It could determine also the dynamic
relationship between different variables in portfolio
management and risk control.
each layer, the transfer function of each neuron, and
the size of the training set. While too small a training
set will prohibit the network from developing
generalized patterns of the inputs, too large a one will
break down the generalized patterns and make the
network sensitive to input noise. This calls for easyto use and easy-to-modify ANN development tools
that are gradually appearing on the market.
C. The output quality of an ANN may be unpredictableThis may not be the case for finding the solution to a
problem with linear constraints in which the solution,
if found, is guaranteed to be the global optimum. A
solution to a non-linear problem reached by the ANN
may not be the global optimum. In a mission-critical
application, one should develop ANN solutions in
parallel with the conventional ones for direct
comparison. Both types of systems should be run for
a period of time, long enough to make sure that the
ANN systems are error-free before they are used in
real situations.
VII. CONCLUSIONS
The field of ANN went through a dormant period, because the
early single layer models were fundamentally flawed. Soon
after, some multi-layer and trainable ANN models emerged.
Despite having some inherent limitations, ANNs have been
increasingly popular since then. They are feasible for those
business applications which require the solution of very
complex system of equations, recognizing patterns from
imperfect inputs, and adapting decisions to changing
environment. Parallel Processing is more needed in this
present time because with the help of parallel processing only
we can save more and more time and money in any work
related to computers and robots.
VI. Limitations of Artificial Neural Networks
REFERENCES
Artificial neural network is undoubtedly a powerful tool for
decision making. But there are several weaknesses in its use.
A. ANN is not a general-purpose problem solver- It is
good at complex numerical computation for the
purposes of solving system of linear or non-linear
equations, organizing data into equivalent classes,
and adapting the solution model to environmental
changes. However, it is not good at such tasks as
calculating payroll, balancing checks, and generating
invoices.
B. There is no structured methodology available-No
structured methodology available for choosing,
developing, training, and verifying an ANN. The
solution quality of an ANN is known to be affected
by the number of layers, the number of neurons at
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