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Predicting amount of saleable products using neural network metamodels of casthouses
conference contributionposted on 2010-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton, Bruce GunnBruce Gunn
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.
EventIEEE International Conference on Control, Automation, Robotics & Vision (11th : 2010 : Singapore)
Pagination2018 - 2023
Place of publicationPiscataway, N.J.
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Publication classificationE1 Full written paper - refereed
Copyright notice2010, IEEE
Title of proceedingsICARCV 2010 : 11th International Conference on Control, Automation, Robotics and Vision
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