TY  - JOUR
A1  - Pietruschka, Ulf
A1  - Brause, Rüdiger W.
T1  - Using growing RBF-nets in rubber industry process control
T2  - Neural computing & applications
N2  - 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
KW  - Adaptive process control
KW  - Parameter estimation
KW  - RBF-nets
KW  - Rubber extrusion
Y1  - 2010
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7963
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-79154
SN  - 0941-0643
SN  - 1433-3058
N1  - © Springer-Verlag London Limited 1999
VL  - 8
IS  - 2
SP  - 95
EP  - 105
PB  - Springer
CY  - London
ER  -