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- W1692937247 abstract "While artificial neural networks and fuzzy systems have both been used as universal approximators, the two approaches have different advantages. For example, neural networks are good at classification and learning, while fuzzy systems can perform inference. To take advantage of such complementary strengths, various hybrid neural-fuzzy systems have been devised. The research reported here involves a new combination of neural and fuzzy systems developed for the protein folding problem, that is, how to estimate the number of topological hydrophobic contacts in the (unknown) most stable conformation of a given sequence of monomer residues. Fuzzy meta-rules are used to generate a series of neural networks for longer and longer input monomer sequences. >" @default.
- W1692937247 created "2016-06-24" @default.
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- W1692937247 date "2002-12-30" @default.
- W1692937247 modified "2023-09-25" @default.
- W1692937247 title "A neural-fuzzy system for the protein folding problem" @default.
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- W1692937247 doi "https://doi.org/10.1109/ifis.1993.324216" @default.
- W1692937247 hasPublicationYear "2002" @default.
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