Soft computing applications in dynamic model identification of polymer extrusion process

Tan, LP, Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, Lai, E and Hull, B, 2004. Soft computing applications in dynamic model identification of polymer extrusion process. Applied Soft Computing, 4 (4), pp. 345-355. ISSN 1568-4946

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Abstract

This paper proposes the application of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy ruled-based system is introduced into the analytical model of the extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing the optimal structure for the sub-models, a hybrid algorithm of genetic algorithm with fuzzy system (GA-Fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions. The developed model is adaptive with its learning ability through the steepest decent error back-propagation algorithm. This ability might help to minimise the deviation of the model prediction when the operational-sensitive parameters adapt to the changing operating environment in the real situation. The model is first evaluated through simulations on the consistency of model prediction to the theoretical analysis. Then, the effectiveness of adaptive sub-models in approximating the operational-sensitive parameters during the operation is further investigated.

Item Type:	Journal article
Publication Title:	Applied Soft Computing
Creators:	Tan, L.P., Lotfi, A., Lai, E. and Hull, B.
Publisher:	Elsevier Science
Place of Publication:	Amsterdam
Date:	2004
Volume:	4
Number:	4
ISSN:	1568-4946
Identifiers:	Number Type 10.1016/j.asoc.2003.10.004 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	EPrints Services
Date Added:	09 Oct 2015 10:17
Last Modified:	09 Jun 2017 13:24
URI:	https://irep.ntu.ac.uk/id/eprint/10621

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