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...
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| Veröffentlicht in: | IEEE transactions on systems, man and cybernetics. Part B, Cybernetics man and cybernetics. Part B, Cybernetics, 1997-12, Vol.27 (6), p.1030-1034 |
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| container_end_page | 1034 |
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| container_issue | 6 |
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| container_title | IEEE transactions on systems, man and cybernetics. Part B, Cybernetics |
| container_volume | 27 |
| creator | Young Moon Park Gwang-Won Kim Hong-Shik Cho Lee, K.Y. |
| description | 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. |
| doi_str_mv | 10.1109/3477.650064 |
| format | Article |
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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.</description><subject>Algorithm design and analysis</subject><subject>Clustering algorithms</subject><subject>Nearest neighbor searches</subject><subject>Pattern analysis</subject><subject>Power system analysis computing</subject><subject>Power system stability</subject><subject>Power system transients</subject><subject>Stability analysis</subject><subject>Transient analysis</subject><subject>Vector quantization</subject><issn>1083-4419</issn><issn>1941-0492</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNqN0b1LxDAYBvAgih-nk5uDZNJBeuarzWU8xC8UXHS1pOlbjaRNTXoc_e_NcYdu6pSE_N4nkAehY0qmlBJ1yYWU0yInpBBbaJ8qQTMiFNtOezLjmRBU7aGDGD8IIYoouYv26IwVPA3vo9c57mCJtXvzwQ7vLW58wA_-3XfQYadHCNiBDp3t3vAyAaz73lmjB-s7PHjc-2UicYwDtDgOurLODiPWnXZjtPEQ7TTaRTjarBP0cnP9fHWXPT7d3l_NHzPD1WzINDSFBCYNqaUxrKlJLdKpAcWrSrK6LoAJlRdKcG4q1WiZM9PISkiRc05qPkHn69w--M8FxKFsbTTgnO7AL2IpuWB5wZhM8uxXyWZKMZpi_4bpdU7_AfOCMp4-fIIu1tAEH2OApuyDbXUYS0rKVZXlqspyXWXSp5vYRdVC_WM33SVwsgYWAL6vN9NfMVehow</recordid><startdate>19971201</startdate><enddate>19971201</enddate><creator>Young Moon Park</creator><creator>Gwang-Won Kim</creator><creator>Hong-Shik Cho</creator><creator>Lee, K.Y.</creator><general>IEEE</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7SP</scope><scope>7TB</scope><scope>FR3</scope><scope>H8D</scope><scope>7X8</scope></search><sort><creationdate>19971201</creationdate><title>A new algorithm for Kohonen layer learning with application to power system stability analysis</title><author>Young Moon Park ; Gwang-Won Kim ; Hong-Shik Cho ; Lee, K.Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c398t-aef67e27c0d7cc2fd0d427cfe93bb72dd6e249569433cb9fa752cf7b4745330d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Algorithm design and analysis</topic><topic>Clustering algorithms</topic><topic>Nearest neighbor searches</topic><topic>Pattern analysis</topic><topic>Power system analysis computing</topic><topic>Power system stability</topic><topic>Power system transients</topic><topic>Stability analysis</topic><topic>Transient analysis</topic><topic>Vector quantization</topic><toplevel>online_resources</toplevel><creatorcontrib>Young Moon Park</creatorcontrib><creatorcontrib>Gwang-Won Kim</creatorcontrib><creatorcontrib>Hong-Shik Cho</creatorcontrib><creatorcontrib>Lee, K.Y.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on systems, man and cybernetics. 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| identifier | ISSN: 1083-4419 |
| ispartof | IEEE transactions on systems, man and cybernetics. Part B, Cybernetics, 1997-12, Vol.27 (6), p.1030-1034 |
| issn | 1083-4419 1941-0492 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_28474315 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Clustering algorithms Nearest neighbor searches Pattern analysis Power system analysis computing Power system stability Power system transients Stability analysis Transient analysis Vector quantization |
| title | A new algorithm for Kohonen layer learning with application to power system stability analysis |
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