Modelling and optimisation control of polymer composite moulding processes using bootstrap aggregated neural network models

Zhang, J; Pantelelis, NG

dc.contributor.author	Zhang, J	en
dc.contributor.author	Pantelelis, NG	en
dc.date.accessioned	2014-03-01T02:47:25Z
dc.date.available	2014-03-01T02:47:25Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33130
dc.subject	bootstrap re-sampling	en
dc.subject	modeling	en
dc.subject	Neural networks	en
dc.subject	optimisation	en
dc.subject	polymer composite moulding	en
dc.subject.other	Bootstrap resampling	en
dc.subject.other	Degree of cure	en
dc.subject.other	Model generalization	en
dc.subject.other	Modelling and optimisation	en
dc.subject.other	Moulding process	en
dc.subject.other	Multiple neural networks	en
dc.subject.other	Neural network model	en
dc.subject.other	Operational data	en
dc.subject.other	Optimal heating	en
dc.subject.other	Optimisations	en
dc.subject.other	Optimization control	en
dc.subject.other	Optimization problems	en
dc.subject.other	Polymer composite	en
dc.subject.other	polymer composite moulding	en
dc.subject.other	Simulated data	en
dc.subject.other	Composite materials	en
dc.subject.other	Models	en
dc.subject.other	Molding	en
dc.subject.other	Optimization	en
dc.subject.other	Polymers	en
dc.subject.other	Neural networks	en
dc.title	Modelling and optimisation control of polymer composite moulding processes using bootstrap aggregated neural network models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICEICE.2011.5777841	en
heal.identifier.secondary	5777841	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICEICE.2011.5777841	en
heal.publicationDate	2011	en
heal.abstract	This paper presents using bootstrap aggregated neural networks for the modelling and optimization control of reactive polymer composite moulding processes. Neural network models for the degree of cure are developed from process operational data. To improve model generalization capability, multiple neural networks are developed from bootstrap re-samples of the original data and are combined. Optimal heating profile is obtained by solving an optimization problem using the neural network model. The proposed method is applied to both simulated data and real industrial data. © 2011 IEEE.	en
heal.journalName	2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings	en
dc.identifier.doi	10.1109/ICEICE.2011.5777841	en
dc.identifier.spage	2363	en
dc.identifier.epage	2366	en