Essay about Neural Networks

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Neural Networks

     A neural network also known as an artificial neural network provides a unique computing architecture whose potential has only begun to be tapped. They are used to address problems that are intractable or cumbersome with traditional methods. These new computing architectures are radically different from the computers that are widely used today. ANN's are massively parallel systems that rely on dense arrangements of interconnections and surprisingly simple processors (Cr95, Ga93).
     Artificial neural networks take their name from the networks of nerve cells in the brain. Although a great deal of biological detail is eliminated in these computing models, the ANN's
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     Neural network architectures are strikingly different from traditional single-processor computers. Traditional Von Neumann machines have a single CPU that performs all of its computations in sequence (He90). A typical CPU is capable of a hundred or more basic commands, including additions, subtractions, loads, and shifts. The commands are executed one at a time, at successive steps of a time clock. In contrast, a neural network processing unit may do only one, or, at most, a few calculations. A summation function is performed on its inputs and incremental changes are made to parameters associated with interconnections. This simple structure nevertheless provides a neural network with the capabilities to classify and recognize patterns, to perform pattern mapping, and to be useful as a computing tool (Vo94).
     The processing power of a neural network is measured mainly be the number of interconnection updates per second. In contrast, Von Neumann machines are benchmarked by the number of instructions that are performed per second, in sequence, by a single processor (He90). Neural networks, during their learning phase, adjust parameters associated with the interconnections between neurons.
Thus, the rate of learning is dependent on the rate of interconnection updates
(Kh90).

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