Abstract
Artificial neural systems can be considered as simplified mathematical models of brain-like systems and they function as parallel distributed computing networks. However, in contrast to conventional computers, which are programmed to perform specific task, most neural networks must be taught, or trained. They can learn new associations, new functional dependencies and new patterns. Although computers outperform both biological and artificial neural systems for tasks based on precise and fast arithmetic operations, artificial neural systems represent the promising new generation of information processing networks.
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Fullér, R. (2000). Artificial neural networks. In: Introduction to Neuro-Fuzzy Systems. Advances in Soft Computing, vol 2. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1852-9_2
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DOI: https://doi.org/10.1007/978-3-7908-1852-9_2
Publisher Name: Physica, Heidelberg
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