Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2004.829304
DC FieldValue
dc.titleNeural-network-based predictive learning control of ram velocity in injection molding
dc.contributor.authorHuang, S.N.
dc.contributor.authorTan, K.K.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-17T02:58:37Z
dc.date.available2014-06-17T02:58:37Z
dc.date.issued2004-08
dc.identifier.citationHuang, 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
dc.identifier.issn10946977
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/56788
dc.description.abstractIn 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCC.2004.829304
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TSMCC.2004.829304
dc.description.sourcetitleIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
dc.description.volume34
dc.description.issue3
dc.description.page363-368
dc.description.codenITCRF
dc.identifier.isiut000222721200012
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