A neural-fuzzy system for the protein folding problem Conference Paper

abstract

• 1993 IEEE. 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.

name of conference

• Third International Conference on Industrial Fuzzy Control and Intelligent Systems

authors

• Daugherity, Walter

published proceedings

• Third International Conference on Industrial Fuzzy Control and Intelligent Systems

author list (cited authors)

• Daugherity, W. C.

citation count

• 0

complete list of authors

• Daugherity, WC

publication date

• January 1993

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Generic Health Relevance

Digital Object Identifier (DOI)

• 10.1109/ifis.1993.324216

International Standard Book Number (ISBN) 10

• 0780314859

International Standard Book Number (ISBN) 13

• 9780780314856

start page

• 47

end page

• 49