Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/58952
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dc.contributor.authorWatts, Michael Johnen
dc.date.issued2010en
dc.identifier.citationInternational Journal of Computational Intelligence and Applications, Special Issue on Neuro-Computing and Hybrid Methods for Evolving Intelligence, 2004; 4(3):299-308en
dc.identifier.issn1469-0268en
dc.identifier.urihttp://hdl.handle.net/2440/58952-
dc.description.abstractA 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 relevanten
dc.description.statementofresponsibilityMichael J. Wattsen
dc.publisherWorld Scientific Publishing Companyen
dc.rightsCopyright © 2004 World Scientific Publishing Co. All rights reserved.en
dc.subjectRule extraction; constructive networks; fuzzy rules; ECoSen
dc.titleFuzzy Rule Extraction from Simple Evolving Connectionist Systemsen
dc.typeJournal articleen
dc.contributor.schoolSchool of Earth and Environmental Sciencesen
dc.identifier.doi10.1142/S146902680400132Xen
Appears in Collections:Earth and Environmental Sciences publications
Environment Institute publications

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