Utilizing radial basis function neural networks to recognize shifts in correlated manufacturing process parameters

Deborah F. Cook; Chih Chou Chiu

Utilizing radial basis function neural networks to recognize shifts in correlated manufacturing process parameters

Deborah F. Cook
, Chih Chou Chiu

經營管理系

研究成果: 會議貢獻 › 文本 › 同行評審

摘要

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because data from these facilities are autocorrelated. Therefore the reduction in process variability obtained through the use of SPC techniques in discrete industries has not been realized in process industries. Techniques are needed to identify process shifts in correlated parameters. Radial basis function neural networks were developed to identify shifts in correlated process parameter values from a papermaking data set. Networks were successful at identifying shifts of 2, 1.5, and 1 standard deviations in the papermaking parameter values.

原文	English
頁面	1769-1771
頁數	3
出版狀態	Published - 1996
事件	Proceedings of the 1996 27th Annual Meeting of the Decision Sciences Institute. Part 2 (of 3) - Orlando, FL, USA 持續時間: 24 11月 1996 → 26 11月 1996

Conference

Conference	Proceedings of the 1996 27th Annual Meeting of the Decision Sciences Institute. Part 2 (of 3)
城市	Orlando, FL, USA
期間	24/11/96 → 26/11/96

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Link to publication in Scopus

引用此

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T2 - Proceedings of the 1996 27th Annual Meeting of the Decision Sciences Institute. Part 2 (of 3)

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Utilizing radial basis function neural networks to recognize shifts in correlated manufacturing process parameters

摘要

Conference

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