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Neural Networks

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 retain enough of the structure observed in the
brain to provide insight into how biological neural processing may work (He90).
Neural networks provide an effective approach for a broad spectrum of
applications. Neural networks excel at problems involving patterns, which
include pattern mapping, pattern completion, and pattern classification (He95).
Neural networks may be applied to translate images into keywords or even
translate financial data into financial predictions (Wo96).
Neural networks utilize a parallel processing structure that has large
numbers of processors and many interconnections between them. These processors
are much simpler than typical central processing units (He90). In a neural
network, each processor is linked to many of its neighbors so that there are
many more interconnections than processors. The power of the neural network
lies in the tremendous number of interconnections (Za93).
ANN's are generating much interest among engineers and scientists.
Artificial neural network models contribute to our understanding of biological
models. They also provide a novel type of parallel processing that has powerful
capabilities and potential for creative hardware implementations, meets the
demand for fast computing hardware, and provides the potential for solving...