Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|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|
|School/Discipline:||School of Earth and Environmental Sciences|
|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.|
|Appears in Collections:||Earth and Environmental Sciences publications|
Environment Institute publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.