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| Titel: |
X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING |
| Auteur: |
Hwarng, H. Brian Hubele, Norma Faris |
| Verschenen in: |
IIE transactions |
| Paginering: | Jaargang 25 (1993) nr. 3 pagina's 27-40 |
| Jaar: |
1993-05-01 |
| Inhoud: |
Back-propagation pattern recognizers (BPPR) are proposed to identify unnatural patterns exhibited on Shewhart control charts. These unnatural patterns, such as cycles and trends, can provide valuable information for real-time process control. In a computer-integrated manufacturing environment, the operator need not routinely monitor the control chart but, rather, can be alerted to patterns by a computer signal generated by the proposed algorithm. In this paper, an off-line analysis is performed to investigate the training and learning speed of these BPPRs on simulated X¯ data. The best configuration of the network is further tested to demonstrate the classification capability of the proposed BPPR. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 13 van 13 gevonden artikelen
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