Neural Netowrks
Neural Networks
Functions and Applications
Submitted By:
Maryam Mustafa
Sarah Karim
05020084
05020259
Neural Netowrks
A Neural Network is an information processing paradigm which is based on the way the
brain processed information. It is composed of highly interconnected processing elements
modeled after the neurons in the brain. These work in unison to solve specific problems.
These networks like the human brain learns by example, and are configured for a specific
function and application. Learning in the human brain functions by adapting to the
synaptic connections that exist between neurons and this is true for neural networks as
well. These artificially neural networks are modeled as device with many inputs and one
output. The neuron has two types of modes: the training mode and the using mode. In the
training mode it can be taught when to fire and when not to for a given set of inputs. In
the using mode, when a taught input pattern is detected at the input, its associated output
becomes the current output. If the input pattern does not belong in the taught list of input
patterns, the firing rule is used to determine whether to fire or not. The firing rule is an
important concept in neural networks and accounts for their high flexibility. A firing rule
determines how one calculates whether a neuron should fire for any input pattern. It
relates to all the input patterns, not only the ones on which the node was trained.
Neural networks, with their 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. Neural networks have been
shown to be particularly useful in solving problems where traditional artificial
intelligence techniques involving symbolic methods have failed or proved inefficient.
Some of the most important real world applications of neural networks are as follows.
Pattern Detection
One of the most widely used areas for neural networks is pattern detection. Pattern
matching includes undersea mine detection; texture analysis; three-dimensional object
recognition; hand-written word recognition; and facial recognition.
One way in which hand written characters are recognized is using feed forward networks.
How this works is that the bitmap pattern of the handwritten character is treated as an
input, with the correct letter or digit as the desired output. Such programs require the user
to train the network by providing the program with their handwritten patterns i.e. learning
by example.
Similarly neural nets have been very successful in facial and voice recognition. They
have also very successfully been used to detect patterns is DNA.
Neural networks in medicine
One of the most researched areas these days in neural networks is the potential for their
use in medicine. Most of the current work being done is mostly on modeling parts of the
human body and recognizing diseases from various scans. Since neural networks learn by
example they are ideal for usage in trying to detect diseases, and so there is no need to
provide any specific algorithm for detection. What is needed is a set of examples that are
representative of all the variations of the disease. The quantity of examples is not as
important as the 'quantity'. The examples need to be selected very carefully if the system
is to perform reliably and efficiently.
Neural Netowrks
Some specific examples of how exactly neural networks are used in medicine are:
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Modeling and Diagnosing the Cardiovascular System. Currently neural networks
are being used experimentally to model the to model the human cardiovascular
system. Building a model of the cardiovascular system of a human and comparing
it with the real time measurements taken from the patient can help in diagnosing
the patient. If this routine is carried out regularly, potential harmful medical
conditions can be detected at an early stage and thus make the process of
combating the disease much easier.
Electronic noses. The implementation of electronic noses through the use of
neural networks is still being researched but has several potential applications in
telemedicine.
Instant Physician. This application was developed in the 1980s and was used to
find the diseases associated with a specific set of symptoms. This was achieved by
a neural net that was “taught” all the symptoms and their related diseases.
Neural Networks and image compression
Because neural nets are designed to take in a large number of inputs and process them
quickly they are ideally suited for image compression. The kind of neural network used
to achieve compression if called a bottleneck type network, and consists of an input layer
and an output layer of equal sizes, with an intermediate layer of smaller size in-between.
The ratio of the size of the input layer to the size of the intermediate layer is - of course the compression ratio.
Neural Networks in Business Applications
Business is a diverted field with several general areas of specialization such as
accounting or financial analysis. Almost any neural network application would fit into
one business area or financial analysis.
There is some potential for using neural networks for business purposes, including
resource allocation and scheduling. There is also a strong potential for using neural
networks for database mining that is, searching for patterns implicit within the explicitly
stored information in databases. Most of the funded work in this area is classified as
proprietary (Stergiou).
Some concrete examples of how neural networks are used in business applications are:
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Financial prediction. Neural networks have been touted as all-powerful tools in
stock-market prediction. Companies such as MJ Futures claim amazing 199.2%
returns over a 2-year period using their neural network prediction methods.
Neural Nets are used to discover trends in market data that humans might not
have noticed and then successfully use these trends in their predictions. Good
results have been achieved by Dean Barr and Walter Loick at LBS Capital
Management using a relatively simple neural network with just 6 financial
indicators as inputs. These inputs include the ADX, which indicates the average
Neural Netowrks
directional movement over the previous 18 days, the current value of the S&P
500, and the net change in the S&P 500 value from five days prior
(http://www-cse.stanford.edu/classes/sophomore-college/projects-00/neuralnetworks/Applications/stocks.html)
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Marketing. The Airline Marketing Tactician is an application that uses various
technologies including neural nets and expert systems. A feed forward network is
used to assist in the marketing control of airline seat allocations. The adaptive
neural approach was open to rule expression and the application's environment
changed rapidly and constantly, which required a continuously adaptive solution.
(Stergiou)
Other examples of use of neural networks in business applications are:
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Currency prediction
Futures prediction
Bond ratings
Business failure prediction
Debt risk assessment
Credit approval
Bank theft
Bank failure
Neural Netowrks
References
Stergiou,C (n.d) Neural Networks.Retrieved on March 31st 2005, from
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#Introduction%20t
o%20neural%20networks
Smith,L(1996).An introduction to Neural Netowrks. Retrieved on March 31st 2005, from
http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html#what
Neural Networks (n.d) Retrieved on March 31st 2005 from http://wwwcse.stanford.edu/classes/sophomore-college/projects-00/neural-networks/Applications/