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| Titel: |
Confirmation testing of the Taguchi methods by artificial neural-networks simulation |
| Auteur: |
Co, H. C. |
| Verschenen in: |
International journal of production research |
| Paginering: | Jaargang 46 (2008) nr. 17 pagina's 4671-4685 |
| Jaar: |
2008-09-01 |
| Inhoud: |
In Taguchi's methods of parameter design, a confirmation test is usually necessary to remove concerns about the choice of control parameters, experimental design, or assumptions about responses. This paper investigated the use of artificial neural-networks simulation to validate the set of control parameters identified as significant through Taguchi's methods, and to verify that the recommended settings for the control parameters are indeed optimal or near-optimal. Using the experimental layout and measured responses from a Taguchi parameter-design experiment, we applied cross-validate training to ascertain that the trained neural-network can reproduce acceptable results on unseen experimental layouts. We then used the trained neural-network to simulate and search for the global optimal settings for the control parameters, and the results compared with the recommended settings from the Taguchi parameter-design experiment. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 51 van 183 gevonden artikelen
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