EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS

Peter Grabusts

Abstract


This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set.


Keywords


neural networks; rule extraction; RBF networks; RULEX algorithm

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References


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DOI: http://dx.doi.org/10.17770/etr2005vol1.2128

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