Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/58953
Type: Journal article
Title: ANN Rule Extraction using Evolutionary Programmed Fuzzy Membership Functions
Author: Watts, M.
Citation: International Journal of Information Technology, 2005; 11(10):45-53
Publisher: International Academy of Sciences
Issue Date: 2005
ISSN: 1305-239X
0218-7957
Statement of
Responsibility: 
Michael J. Watts
Abstract: An algorithm is presented that uses evolutionary programming to construct fuzzy membership functions that are used to extract Zadeh-Mamdani fuzzy rules from a constructive neural network. The algorithm has potential applications in fields such as data mining and knowledge-based decision support systems. Evaluation of the algorithm over two well known benchmark data sets shows that while the results are promising, some problems are apparent. These problems provide avenues for further research.
Keywords: Evolving Connectionist Systems; ECoS; Simple Evolving Connectionist System; SECoS; fuzzy rule extraction; evolutionary programming
RMID: 0020103727
Description (link): http://www.intjit.org/journal/volume/11/10/editorial.html
Published version: http://www.intjit.org/journal/volume//11/10/1110_6.pdf
Appears in Collections:Earth and Environmental Sciences publications
Environment Institute publications

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