Abstract
It is possible to eliminate the reset/resonance cycle of many of the ART algorithms without modifying their learning or classification rules. The enabling principle is to check the vigilance criterion before comparing the choice functions, rather than vice versa. This re-ordering of operations reduces the computational complexity. It leads to a simpler neural architecture that is better suited to parallel implementation and facilitates understanding the algorithm.
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Blume, M. (2000). An Efficient Mapping of Fuzzy Art Onto a Neural Architecture. In: Jain, L.C., Lazzerini, B., Halici, U. (eds) Innovations in ART Neural Networks. Studies in Fuzziness and Soft Computing, vol 43. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1857-4_2
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DOI: https://doi.org/10.1007/978-3-7908-1857-4_2
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