DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, YS | ko |
dc.contributor.author | Bien, Zeung nam | ko |
dc.date.accessioned | 2013-03-08T14:47:43Z | - |
dc.date.available | 2013-03-08T14:47:43Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2005-10 | - |
dc.identifier.citation | IRANIAN JOURNAL OF FUZZY SYSTEMS, v.2, no.2, pp.1 - 13 | - |
dc.identifier.issn | 1735-0654 | - |
dc.identifier.uri | http://hdl.handle.net/10203/93311 | - |
dc.description.abstract | The proposed IAFC neural networks have both stability and plasticity because they use a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network. The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzy leaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzy membership values. The supervised IAFC neural networks are the supervised neural networks which use the fuzzified versions of Learning Vector Quantization (LVQ). In this paper, several important adaptive learning algorithms are compared from the viewpoint of structure and learning rule. The performances of several adaptive learning algorithms are compared using Iris data set. | - |
dc.language | English | - |
dc.publisher | Univ Sistan & Baluchestan | - |
dc.title | Integrated Adaptive Fuzzy Clustering (IAFC) Neural Networks Using Fuzzy Learning Rules | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.citation.volume | 2 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 1 | - |
dc.citation.endingpage | 13 | - |
dc.citation.publicationname | IRANIAN JOURNAL OF FUZZY SYSTEMS | - |
dc.contributor.localauthor | Bien, Zeung nam | - |
dc.contributor.nonIdAuthor | Kim, YS | - |
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