Abstract
Artificial Neural Networks (ANNs) are massively parallel interconnected networks of simple (usually adaptive) nodes which are intended to interact with objects of the real world in the same way as biological nervous systems do [1]. The interest in these networks is due to the general opinion that they are able to perform some complicated and creative tasks, such as pattern recognition, similar to the way they are performed by human brains [2-3]. The implementations of these tasks by traditional computing methods have only reached relatively low performances in some limited aspects or environments. Nevertheless, as neural systems show some properties, like association, generalization, parallel searching, and adaptation to changes in the environment, which are analogous to human brain properties, they promise improved results.
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Zhang, D., Kamel, M., Elmasry, M.I. (1994). A Parallel ANN Architecture for Fuzzy Clustering. In: Elmasry, M.I. (eds) VLSI Artificial Neural Networks Engineering. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-2766-4_8
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DOI: https://doi.org/10.1007/978-1-4615-2766-4_8
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