neurons,has also been developed. Each neuron in the network has one or more inputs and produces an output each input has a weighting factor, which modifies the value entering the neuron. The neuron mathematically manipulates the inputs, and outputs the result. The neural network is simply neurons joined together, with the output from one neuron becoming input to others until the final output is reached. The network learns when examples (with known results) are presented to it the weighting factors are adjusted—either through human intervention or by a programmed algorithm—to bring the final output closer to the known result.
Neural networks are good at providing very fast, very close approximations of the correct answer. Although they are not as well suited as conventional computers for performing mathematical calculations or moving and comparing alphabetic characters, neural networks excel at recognizing shapes or patterns, learning from experience, or sorting relevant data from irrelevant. Their applications can be categorized into classification, recognition and identification, assessment, monitoring and control, and forecasting and prediction. Among the tasks for which they are well suited are handwriting recognition, foreign language translation, process control, financial forecasting, medical data interpretation, artificial intelligence research, and parallel processing implementations of conventional processing tasks. In an ironic reversal, neural networks are being used to model disorders of the brain in an effort to discover better therapeutic strategies.
See Y. Burnod, An Adaptive Neural Network: The Cerebral Cortex (1990) J. S. Judd, Neural Network Design and the Complexity of Learning (1990) S. I. Gallant, Neural Network Learning and Expert Systems (1993) L. Medsker, Hybrid Neural Network and Expert Systems (1994) R. L. Harvey, Neural Network Principles (1994).
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