A new algorithm for Kohonen layer learning with application to power system stability analysis
In certain classification problems, input patterns are not distributed in a clustering manner but distributed uniformly in an input space and there exist certain critical hyperplanes called decision boundaries. Since learning vector quantization (LVQ) classifies an input vector based on the nearest...
| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society. - 1996. - 27(1997), 6 vom: 15., Seite 1030-4 |
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| Weitere Verfasser: | , , |
| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
1997
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In certain classification problems, input patterns are not distributed in a clustering manner but distributed uniformly in an input space and there exist certain critical hyperplanes called decision boundaries. Since learning vector quantization (LVQ) classifies an input vector based on the nearest neighbor, the codebook vectors away from the decision boundaries are redundant. This paper presents an alternative algorithm called boundary search algorithm (BSA) for the purpose of solving this redundancy problem. The BSA finds a fixed number of codebook vectors near decision boundaries by selecting appropriate training vectors. It is found to be more efficient compared with LVQ and its validity is demonstrated with satisfaction in the transient stability analysis of a power system |
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| Beschreibung: | Date Completed 02.10.2012 Date Revised 11.02.2008 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0492 |