Abstract
Artificial hydrocarbon networks (AHN) algorithm builds and trains a model for any given system. However, that model considers a single-input-and-single-output (SISO) system and a fixed number of molecules. These assumptions limit the performance of the obtained model. For example, systems that are not SISO cannot be handled easy with artificial hydrocarbon networks, or the number of molecules is difficult to determine, except from experience tuning or trail-and-error.
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Ponce-Espinosa, H., Ponce-Cruz, P., Molina, A. (2014). Enhancements of Artificial Hydrocarbon Networks. In: Artificial Organic Networks. Studies in Computational Intelligence, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-02472-1_5
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DOI: https://doi.org/10.1007/978-3-319-02472-1_5
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Online ISBN: 978-3-319-02472-1
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