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

Department of Business Management

Research output: Contribution to conference › Paper › peer-review

Abstract

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.

Original language	English
Pages	1769-1771
Number of pages	3
State	Published - 1996
Event	Proceedings of the 1996 27th Annual Meeting of the Decision Sciences Institute. Part 2 (of 3) - Orlando, FL, USA Duration: 24 Nov 1996 → 26 Nov 1996

Conference

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

Cite this

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

Y2 - 24 November 1996 through 26 November 1996

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

Abstract

Conference

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