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Authors: Jie Zhang 1 and Nikos G. Pantelelis 2

Affiliations: 1 School of Chemical Engineering and Advanced Materials and Newcastle University, United Kingdom ; 2 National Technical University of Athens, Greece

ISBN: 978-989-8425-84-3

Keyword(s): Neural networks, Polymer composite moulding, Bootstrap re-sampling, Modelling, Optimisation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper presents using bootstrap aggregated neural networks for the modelling and optimisation control of reactive polymer composite moulding processes. Bootstrap aggregated neural networks combine multiple neural networks developed from bootstrap re-sampling replications of the original training data in order to enhance model prediction and generalisation capability. Neural network models for modelling the degree of cure (through modelling the measured resistance) are developed from real industrial process operational data. Both static and dynamic models are developed and the developed neural network models are validated on unseen process operation data. The bootstrap aggregated neural network models give accurate and reliable predictions than single neural networks. Optimal heating profile is obtained by solving an optimisation problem using the dynamic neural network model. The model prediction confidence bound is incorporated in the optimisation objective function in order to e nhance the reliability of the calculated optimal control profile. In addition to maximise the final degree of cure, model prediction confidence bound is minimised. Application results on a simulated polymer composite moulding process demonstrate that the proposed reliable optimisation control strategy is effective. (More)

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Paper citation in several formats:
Zhang, J. and G. Pantelelis, N. (2011). RELIABLE MODELLING AND OPTIMISATION CONTROL OF REACTIVE POLYMER COMPOSITE MOULDING PROCESSES USING BOOTSTRAP AGGREGATED NEURAL NETWORK MODELS.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 236-241. DOI: 10.5220/0003682602360241

@conference{ncta11,
author={Jie Zhang. and Nikos G. Pantelelis.},
title={RELIABLE MODELLING AND OPTIMISATION CONTROL OF REACTIVE POLYMER COMPOSITE MOULDING PROCESSES USING BOOTSTRAP AGGREGATED NEURAL NETWORK MODELS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={236-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003682602360241},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - RELIABLE MODELLING AND OPTIMISATION CONTROL OF REACTIVE POLYMER COMPOSITE MOULDING PROCESSES USING BOOTSTRAP AGGREGATED NEURAL NETWORK MODELS
SN - 978-989-8425-84-3
AU - Zhang, J.
AU - G. Pantelelis, N.
PY - 2011
SP - 236
EP - 241
DO - 10.5220/0003682602360241

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