Using growing RBF-nets in rubber industry process control

  • This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the RBF net learning algorithm are developed, which uses a growing number of RBF units to compensate the approximation error up to the desired error limit. Its performance is shown for simple analytic examples. Then the paper describes the modelling of the industrial problem. Simulations show good results, even when using only a few training samples. The paper is concluded by a discussion of possible systematic error influences, improvements and potential generalisation benefits. Keywords: Adaptive process control; Parameter estimation; RBF-nets; Rubber extrusion

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Author:Ulf Pietruschka, Rüdiger W. BrauseGND
URN:urn:nbn:de:hebis:30-79154
DOI:https://doi.org/10.1007/s005210050012
ISSN:0941-0643
ISSN:1433-3058
Parent Title (German):Neural computing & applications
Publisher:Springer
Place of publication:London
Document Type:Article
Language:English
Date of Publication (online):2010/09/08
Year of first Publication:1999
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2010/09/08
Tag:Adaptive process control; Parameter estimation; RBF-nets; Rubber extrusion
Volume:8
Issue:2
Page Number:11
First Page:95
Last Page:105
Note:
© Springer-Verlag London Limited 1999
Source:Neural computing & Applications, 8, S. 95-105
HeBIS-PPN:227735862
Institutes:Informatik und Mathematik / Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht

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