Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency
نویسندگان
چکیده
In a Bayesian framework, we give a principled account of how domainspecific prior knowledge such as imperfect analytic domain theories can be optimally incorporated into networks of locally-tuned units: by choosing a specific architecture and by applying a specific training regimen. Our method proved successful in overcoming the data deficiency problem in a large-scale application to devise a neural control for a hot line rolling mill. It achieves in this application significantly higher accuracy than optimally-tuned standard algorithms such as sigmoidal backpropagation, and outperforms the state-of-the-art solution.
منابع مشابه
Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deeciency
In a Bayesian framework, we give a principled account of how domain-speciic prior knowledge such as imperfect analytic domain theories can be optimally incorporated into networks of locally-tuned units: by choosing a speciic architecture and by applying a speciic training regimen. Our method proved successful in overcoming the data deeciency problem in a large-scale application to devise a neur...
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