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Applications of Neural Networks
Introduction:
There are two basic kinds of neural networks that exist in society today:
1. Biological neural network.
Biological neural networks refer to the arrangement of the biological neurons
within the human/animal nervous system, which primarily consists of the brain
and the spinal cord. These neurons work in harmony to carry out specific
functions of the body, including learning, problem solving and task functioning.
2. Artificial neural network
Artificial neural networks try to mimic the biological neural networks, both in
functioning and architecture, so as to bring the same kind of learning capabilities
to the machine, as is available to the humans/animals. ANN (artificial neural
network) use a number of highly connected neurons which work on the same task
to come to a result. Mostly the ANNs, like people, learn by example and involve
the manipulation of the connections between neurons to adjust the result.
Applications:
The different types of networks in ANNs involve different architecture which are of use
in different applications. These are highlighted below:
1. ADALINE Network: The ADALINE network is primarily useful in pattern
recognition tasks. It is essentially a single layer back propagation network. For an
example consisting of classifying digits from 0-9, when trained on this pattern
recognition task, the aim is to classify a bitmap representation of the digits 0-9
into the corresponding classes. Adaline is limited in its capabilities for the
network only recognizes the exact training patterns. However better fault
tolerance can be achieved using multi layers in the back propagation network.
2. BPN Network: BPN stands for back propagation network. It is exceptionally
useful in time series forecasting, e.g. predicting the annual number of sunspots.
This kind of network implements the multi-layer back propagation network with
bias terms and momentum. It is used to detect structure in time-series, which is
presented to the network using a simple tapped delay-line memory. The program
learns to predict future activity from historical data. To avoid over training, the
learning procedure is terminated by the stop training method.
3. HOPFIELD Network: The Hopfield network is useful in Auto associative
memory tasks. The network is used as an auto associative memory and stores and
recalls a set of bitmap images. Images are stored by calculating the result from a
corresponding weight matrix. Thereafter, the memory will settle on exactly the
stored image which is nearest to the starting configuration by means of Hamming
distance. Thus the recall on an incomplete or corrupted image is also very much
appreciable.
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4. BAM Network: BAM stands for Bi directional associative memory. Its role in
ANNs is found in the Hetero Associative memory tasks like associations of name
with their corresponding phone numbers. The BAM is a generalization of the
Hopfield model so that a hetero associative memory can be implemented. After
saving a set of examples, the memory returns the associative item on entering the
other associative item. The memory even shows a limited degree of faulttolerance in case of corrupted input patterns.
5. BOLTZMAN Network: The Boltzman model is basically used in optimization
tasks for e.g. the traveling salesman problem. It is basically a stochastic model of
the HOPFIELD model. The network dynamics incorporate a random component
in correspondence with a given finite temperature. It starts with a high
temperature and gradually cools down to a lower stable temperature which turns
out to be the global minimum. This process is called simulated annealing. The
network is then used to solve well-known optimization problems.
6. CPN Network: CPN stands for Counter propagation network. This kind of
network works well with vision applications. It is a competitive network and
functions as a self-programming lookup table with the additional ability to
interpolate between entries. The performance of the network is a little limited due
to the low resolution of the images presented.
7. SOM network: SOM stands for Self organizing maps. These kinds of networks
are mostly used in control applications e.g. a pole balancing problem. It is able to
form a topological mapping between its inputs and outputs and then uses the
modifications of the weights of the neurons to get to the best mapping between
the two.
8. ART1 Network: ART stands for Adaptive Resonance Theory. This kind of
networks work best for Brain modeling tasks. It is able to adapt to new patterns
while remaining stable at previously seen patterns.
In practice ANNs are used in the following places:
 Sales forecasting
 Industrial process control
 Customer research
 Data validation
 Risk management
 Target marketing
 Bio medical systems
 Modeling and diagnosing the cardiovascular system
 Electronic noses
 Credit evaluations
 Marketing