Proceedings of the Korean Society of Precision Engineering Conference (한국정밀공학회:학술대회논문집)
- 1996.04a
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- Pages.403-413
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- 1996
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- 2005-8446(pISSN)
Self-organizing neuro-tracking of non-stationary manufacturing processes
- Wang, Gi-Nam (Department of Industrial Engineering Ajou University) ;
- Go, Young-Cheol (Department of Industrial Engineering Ajou University)
- Published : 1996.04.01
Abstract
Two-phase self-organizing neuro-modeling (SONM). the global SONM and local SONM, is designed for tracking non-stationary manufacturing processes. Radial basis function (RBF) neural network is employed, and self-tuning estimator is also developed for the determination of RBF network parameters on-line. A pattern recognition approach is presented for identifying a correct RBF neural network, which is used for identifying current manufacturing processes. Experimental results showed that the proposed approach is suitable for tracking non-stationary processes.
Keywords