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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pham, D.T. Otri, S. Ghanbarzadeh, A. Koc, E. |
| Copyright Year | 2005 |
| Description | Author affiliation: Manuf. Eng. Centre, Cardiff Univ. (Pham, D.T.; Otri, S.; Ghanbarzadeh, A.; Koc, E.) |
| Abstract | Control charts are employed in manufacturing industry for statistical process control (SPC). It is possible to detect incipient problems and prevent a process from going out of control by identifying the type of patterns displayed by the control charts. Various techniques have been applied to this control chart pattern recognition task. This paper presents the use of learning vector quantisation (LVQ) networks for recognising patterns in control charts. The LVQ networks were trained, not by applying standard training algorithms, but by employing a new optimisation algorithm developed by the authors. The algorithm, called the bees algorithm, is inspired by the food foraging behaviour of honey bees. The paper first describes the bees algorithm and explains how the algorithm is employed to train LVQ networks. It then discusses the recognition of control chart patterns by LVQ networks optimised using the bees algorithm |
| Starting Page | 1624 |
| Ending Page | 1629 |
| File Size | 1361205 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780395212 |
| DOI | 10.1109/ICTTA.2006.1684627 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-04-24 |
| Publisher Place | Syria |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bees Algorithm Vector quantization Process control LVQ Neural Networks Pattern recognition Control Charts Industrial training Manufacturing industries Manufacturing processes Pattern Recognition Neural networks Control charts Standards development Monitoring |
| Content Type | Text |
| Resource Type | Article |
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