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標題: Mold temperature control of a rubber injection-molding machine by TSK-type recurrent neural fuzzy network
作者: Juang, C.F.
Huang, S.T.
Duh, F.B.
關鍵字: recurrent fuzzy networks;neural fuzzy networks;fuzzy control;direct;inverse control;temperature control;generalized predictive control;inference network;systems;identification
Project: Neurocomputing
期刊/報告no:: Neurocomputing, Volume 70, Issue 1-3, Page(s) 559-567.
Practical mold temperature control of a rubber injection-molding machine is studied in this paper. The controller used is a recurrent fuzzy network called Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN), which is characterized by its recurrent structure, on-line structure and parameter learning. Due to the powerful learning ability of TRFN, a simple controller design scheme using direct inverse configuration is proposed. With recurrent structure in TRFN, no a priori knowledge of the molding machine order is required. The designed TRFN controller performs well even if the sampling interval is different from the original one used for training. The design of TRFN consists of off-line and oil-line training. For off-line learning, structure and parameter of TRFN are learned, and the consequent part parameters are tuned by Kalman filter algorithm. On-line learning is performed to fine tune the consequent parameters of TRFN and achieve a better control performance with the use a simple gradient descent algorithm. Practical experiments and comparisons with other types of controllers demonstrate the performance of the proposed TRFN controller. (c) 2006 Elsevier B.V. All rights reserved.
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2005.11.003
Appears in Collections:電機工程學系所

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