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Artificial Neural Networks Artificial Neural Networks Artificial Neural

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					Artificial Neural Networks
Artificial Neural Networks (ANNs) are biologically inspired. Specifically, they borrow ideas from the manner in which

the human brain works. The human brain is composed of special cells called neurons. Estimates of the number of

neurons in a human brain cover a wide range (up to 150 billion), and there are more than a hundred different kinds of

neurons, separated into groups called networks. Each network contains several thousand neurons that are highly

interconnected. Thus, the brain can be viewed as a collection of neural networks.


Today's ANNs, whose application is referred to as neural computing, use a very limited set of concepts from

biological neural systems. The goal is to simulate massive parallel processes that involve processing elements

interconnected in a network architecture. The artificial neuron receives inputs analogous to the electrochemical

impulses biological neurons receive from other neurons. The output of the artificial neuron corresponds to signals

sent out from a biological neuron. These artificial signal can be changed, like the signals from the human brain.

Neurons in an ANN receive information from other neurons or from external source, transform or process the

information, and pass it on to other neurons or as external outputs.


The manner in which an ANN processes information depends on its structure and on the algorithm used to process

the information.


Benefits And Applications Of Neural Networks


The value of neural network technology includes its usefulness for pattern recognition, learning, and the interpretation

of incomplete and "noisy" inputs.


Neural networks have the potential to provide some of the human characteristics of problem solving that are difficult

to simulate using the logical, analytical techniques of DSS or even expert systems. One of these characteristics is

pattern recognition. Neural networks can analyze large quantities of data to establish patterns and characteristics in

situations where the logic and rules are not known. An example would be loan applications. By reviewing many

historical cases of applicants questionnaires and the "yes or no" decisions made, the ANN can create "patterns" or

"profiles" of applications that should be approved or denied. A new application can then matched by the computer

against the pattern. If it comes close enough, the computer classifies it as a "yes" or "no"; otherwise it goes to a

human for a decision. Neural networks are especially useful for financial applications such as determining when to

buy or sell stock, predicting bankruptcy, and predicting exchange rates.


Beyond its role as an alternative computing mechanism, and in data mining, neural computing can be combined with

other computer-based information systems to produce powerful hybrid systems.
Neural computing is emerging as an effective technology in pattern recognition. This capability is being translated to

many applications and is sometimes integrated with fuzzy logic

				
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