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https://hdl.handle.net/2440/58952
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Type: | Journal article |
Title: | Fuzzy Rule Extraction from Simple Evolving Connectionist Systems |
Author: | Watts, Michael John |
Citation: | International Journal of Computational Intelligence and Applications, Special Issue on Neuro-Computing and Hybrid Methods for Evolving Intelligence, 2004; 4(3):299-308 |
Publisher: | World Scientific Publishing Company |
Issue Date: | 2010 |
ISSN: | 1469-0268 |
School/Discipline: | School of Earth and Environmental Sciences |
Statement of Responsibility: | Michael J. Watts |
Abstract: | A method for extracting Zadeh–Mamdani fuzzy rules from a minimalist constructive neural network model is described. The network contains no embedded fuzzy logic elements. The rule extraction algorithm needs no modification of the neural network architecture. No modification of the network learning algorithm is required, nor is it necessary to retain any training examples. The algorithm is illustrated on two well known benchmark data sets and compared with a relevant |
Keywords: | Rule extraction; constructive networks; fuzzy rules; ECoS |
Rights: | Copyright © 2004 World Scientific Publishing Co. All rights reserved. |
DOI: | 10.1142/S146902680400132X |
Appears in Collections: | Earth and Environmental Sciences publications Environment Institute publications |
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