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Predicting amount of saleable products using neural network metamodels of casthouses
conference contribution
posted on 2010-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton, Bruce GunnBruce GunnThis 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.
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Event
IEEE International Conference on Control, Automation, Robotics & Vision (11th : 2010 : Singapore)Pagination
2018 - 2023Publisher
IEEELocation
SingaporePlace of publication
Piscataway, N.J.Start date
2010-12-07End date
2010-12-10Language
engNotes
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E1 Full written paper - refereedCopyright notice
2010, IEEETitle of proceedings
ICARCV 2010 : 11th International Conference on Control, Automation, Robotics and VisionUsage metrics
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