Control chart pattern recognition using learning vector quantization networks

Pattern recognition systems using neural networks for discriminating between different types of control chart patterns are discussed. A class of pattern recognizers based on the Learning Vector Quantization (LVQ) network is described. A procedure to increase the classification accuracy and decrease...

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Bibliographic Details
Published inInternational journal of production research Vol. 32; no. 3; pp. 721 - 729
Main Authors PHAM, D. T., OZTEMEL, E.
Format Journal Article
LanguageEnglish
Published London Taylor & Francis Group 01.03.1994
Washington, DC Taylor & Francis
Subjects
Applied sciences
Exact sciences and technology
Operational research and scientific management
Operational research. Management science
Reliability theory. Replacement problems
Learning
Control chart
Pattern recognition
Neural network
Vector quantization
Quality control
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Summary:Pattern recognition systems using neural networks for discriminating between different types of control chart patterns are discussed. A class of pattern recognizers based on the Learning Vector Quantization (LVQ) network is described. A procedure to increase the classification accuracy and decrease the learning time for LVQ networks is presented. The results of control chart pattern recognition experiments using both existing LVQ networks and an LVQ network implementing the proposed procedure are given.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0020-7543
1366-588X
DOI:10.1080/00207549408956963