Please use this identifier to cite or link to this item:
https://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 |
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: | Aurora harvest 5 Earth and Environmental Sciences publications Environment Institute publications |
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