| Neural
Networks And Their Applications |
Definition
An 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. It is composed of a large number of highly
interconnected processing elements (neurons) 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 neurons. 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 several eras. Many important advances have been
boosted by the use of inexpensive computer emulations. The first artificial neuron
was produced in 1943 by the neurophysiologist Warren McCulloch and the logician
Walter Pitts.
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 analyze. 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 organization 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 have been
successfully applied to broad spectrum of data-intensive applications, such as:
1.Voice Recognition - Transcribing spoken words into ASCII text.
2.Target
Recognition - Military application which uses video and/or infrared image data
to determine if an enemy target is present. 3.Medical
Diagnosis - Assisting doctors with their diagnosis by analyzing the reported symptoms
and/or image data such as MRIs or X-rays.
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