Please use this identifier to cite or link to this item: http://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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