Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency
Abstract
In a Bayesian framework, we give a principled account of how domain(cid:173) specific 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.
Cite
Text
Röscheisen et al. "Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency." Neural Information Processing Systems, 1991.Markdown
[Röscheisen et al. "Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/roscheisen1991neurips-neural/)BibTeX
@inproceedings{roscheisen1991neurips-neural,
title = {{Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency}},
author = {Röscheisen, Martin and Hofmann, Reimar and Tresp, Volker},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {659-666},
url = {https://mlanthology.org/neurips/1991/roscheisen1991neurips-neural/}
}