Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2004.829304
Title: Neural-network-based predictive learning control of ram velocity in injection molding
Authors: Huang, S.N. 
Tan, K.K. 
Lee, T.H. 
Issue Date: Aug-2004
Source: Huang, S.N., Tan, K.K., Lee, T.H. (2004-08). Neural-network-based predictive learning control of ram velocity in injection molding. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 34 (3) : 363-368. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2004.829304
Abstract: In this paper, we develop a predictive learning controller for ram velocity of injection molding based on neural networks. We first introduce a model of describing the injection molding, including the time horizon and the batch index. The feedback control plus biased function is proposed for controlling this plant. More specifically, a radial basis function (RBF) network is used to approximate the biased function based on the time horizon. The weights in the RBF are determined by a predictive control scheme based on the batch index. For this algorithm, relevant convergence is investigated. Simulation results reveal that the proposed control can achieve our claims. © 2004 IEEE.
Source Title: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
URI: http://scholarbank.nus.edu.sg/handle/10635/56788
ISSN: 10946977
DOI: 10.1109/TSMCC.2004.829304
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