Neural networks

What is a Neural Network?
An Artificial 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 systems.It is composed
of a large number of highly interconnected processing elements(neurones)working in unison
to solve specific problems.ANNs,like people,learn by example.An ANN is configured for a
specific application,such as pattern recognition or data classification,through a learning
process.Learning in biological systems involves adjustments to the synaptic connections that
exist between the neurones.This is true of ANNs as well.

Historical Background
Neural network simulations appear to be a recent development.However,this field
was established before the advent of computers,and has survived at least one major setback
and several eras.Many importand advances have been boosted by the use of inexpensive
computer emulations.Following an initial period of enthusiasm,the field survived a period
of frustration and disrepute.During this period when funding and professional support was
minimal,important advances were made by relatively few reserchers.These pioneers were able
to develop convincing technology which surpassed the limitations identified by Minsky and
Papert,published a book (in 1969) in which they summed up a general feeling of frustration
(against neural networks) among researchers,and was thus accepted by most without further
analysis.Currently,the neural network field enjoys a resurgence of interest and a
corresponding increase in funding.The first artificial neuron was produced in 1943 by the
neurophysiologist Warren McCulloch and the logician Walter Pits.But the technology available
at that time did not allow them to do too much.

Why use Neural Networks?
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.A trained neural network can be
thought of as an"expert"in the category of information it has been given to analyse.This
expert can then be used to provide projections given new situations of interest and answer
"what if"questions.
Other advantages include:
1.Adaptive learning:An ability to learn how to do tasks based on the data given for training
or initial experience.
2.Self-Organisation:An ANN can create its own organisation or representation of the
information it receives during learning time.
3.Real Time Operation:ANN computations may be carried out in parallel,and special hardware
devices are being designed and manufactured which take advantage of this capability.
4.Fault Tolerance via Redundant Information Coding:Partial destruction of a network leads to
the corresponding degradation of performance.However,some network capabilities may be
retained even with major network damage.

Neural Networks versus Conventional Computers
Neural networks take a different approach to problem solving than that of
conventional computers.Conventional computers use an algorithmic approach i.e.the computer
Follows a set of instructions in order to solve a problem.Unless the specific steps that the
computer needs to follow are known the computer cannot solve the problem.That restricts the
problem solving capability of conventional computers to problems that we already understand
and know how to solve.But computers would be so much more useful if they could do things
that we don't exactly know how to do.Neural networks process information in a similar way
the human brain dose.The network is composed of a large number of highly interconnected
processing elements(neurones)working in parallel to solve a specific problem.Neural networks
learn by example.They cannot be programmed to perform a specific task.The examples must be
selected carefully otherwise useful time is wasted or even worse the network might be
functioning incorrectly.The disadvantage is that because the network finds out how to solve
the problem by itself,its operation can be unpredictable.
On the other hand,conventional computers use a cognitive approach to problem
solving;the way the problem is to solved must be known and stated in small unambiguous
instructions.These instructions are then converted to a high level language program and then
into machines are totally predictable;if anything goes wrong is due to a software or
hardware fault.
Neural networks and conventional algorithmic computers are not in competition but
complement each other.There are tasks are more suited to an algorithmic approach like
arithmetic operations and tasks that are more suited to neural networks.Even more,a large
number of tasks,require systems that use a combination of the two approaches (normally a
conventional computer is used to supervise the neural network)in order to perform at maximum
efficiency.