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Predicting amount of saleable products using neural network metamodels of casthouses

Khosravi, Abbas, Nahavandi, Saeid, Creighton, Doug and Gunn, Bruce 2010, Predicting amount of saleable products using neural network metamodels of casthouses, in ICARCV 2010 : 11th International Conference on Control, Automation, Robotics and Vision, IEEE, Piscataway, N.J., pp. 2018-2023.

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Title Predicting amount of saleable products using neural network metamodels of casthouses
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, Saeid
Creighton, DougORCID iD for Creighton, Doug orcid.org/0000-0002-9217-1231
Gunn, Bruce
Conference name IEEE International Conference on Control, Automation, Robotics & Vision (11th : 2010 : Singapore)
Conference location Singapore
Conference dates 7-10 Dec. 2010
Title of proceedings ICARCV 2010 : 11th International Conference on Control, Automation, Robotics and Vision
Editor(s) [Unknown]
Publication date 2010
Conference series International Conference on Control, Automation, Robotics and Vision
Start page 2018
End page 2023
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary This study aims at developing abstract metamodels for approximating highly nonlinear relationships within a metal casting plant. Metal casting product quality nonlinearly depends on many controllable and uncontrollable factors. For improving the productivity of the system, it is vital for operation planners to predict in advance the amount of high quality products. Neural networks metamodels are developed and applied in this study for predicting the amount of saleable products. Training of metamodels is done using the Levenberg-Marquardt and Bayesian learning methods. Statistical measures are calculated for the developed metamodels over a grid of neural network structures. Demonstrated results indicate that Bayesian-based neural network metamodels outperform the Levenberg-Marquardt-based metamodels in terms of both prediction accuracy and robustness to the metamodel complexity. In contrast, the latter metamodels are computationally less expensive and generate the results more quickly.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 861203 Metal Castings
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034529

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.