Gabrys, B. and Bargiela, A., 2000. General fuzzy min-max neural network for clustering and classification. Neural Networks, IEEE Transactions on, 11 (3), pp. 769-783.
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This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given.
|Uncontrolled Keywords:||fuzzy neural nets, learning (artificial intelligence), minimax techniques, pattern classification, pattern clusteringGFMM neural network, classification, clustering, decision boundaries, expansion-contraction process, general fuzzy min-max neural network, hyperbox fuzzy sets, leakage detection, leakage identification, lower bounds, supervised learning, training algorithm, unsupervised learning, upper bounds, water distribution systems|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||Professor Bogdan Gabrys|
|Deposited On:||29 Jan 2009 18:06|
|Last Modified:||07 Mar 2013 15:06|
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