首页|Northeastern University Reports Findings in Machine Learning (Rolling theory-guided prediction of hot-rolled plate width based on parameter transfer strategy)
Northeastern University Reports Findings in Machine Learning (Rolling theory-guided prediction of hot-rolled plate width based on parameter transfer strategy)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
New research on Machine Learning is the subject of a report. According to news reporting originating in Shenyang, People's Republic of China, by NewsRx journalists, research stated, "Machine learning performs well in many problems. However, the tendency to generate predictions that violate theoretical knowledge makes it difficult to apply to practical processing." The news reporters obtained a quote from the research from Northeastern University, "To resolve this situation, this paper combines domain knowledge with a data-driven model, proposes a theory-guided machine learning framework based on a parameter transfer strategy, and applies it to the width prediction of plates after multiple passes of hot rolling. The framework applies a swarm optimization algorithm to the original theoretical model and generates numerous highly-physical consistent samples. The established deep neural network (DNN) model is trained with simulated data, and the parameters are fine-tuned using a parameter transfer strategy combined with actual data to ensure excellent adaptation to the actual environment based on adequate learning of theoretical knowledge. In tests, the proposed model had the best overall prediction performance in this paper. Meanwhile, the developed model is consistent with the existing perception of rolling theory."
ShenyangPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
