In the cold tandem rolling process, the product quality and yield are affected by the accuracy of rolling force prediction directly. Fix prediction model is not applicable to the multi-operating conditions rolling environment. In addition, appropriate samples can be hardly selected by a single similarity measure because of the insufficient process knowledge. In order to solve these issues, an ensemble just-in-time-learning modeling method based on multi-weighted similarity measures (MWS-EJITL) is proposed. Firstly, multi-weighted similarity measures is used to select relevant samples. Then, the local model is constructed and the output value of the query data is estimated. Finally, the ensemble learning strategy is adopted to integrate the outputs of each local model. On this basis, the cumulative similarity factor is introduced to optimize the number of samples of local modeling, and the similarity threshold is set to update the local model adaptively. The rolling force prediction experiment verify the effectiveness and accuracy of MWS-EJITL method.

在冷连轧过程中，轧制力预测的准确性直接影响产品质量和成品率。修复预测模型不适用于多工况滚动环境。此外，由于过程知识不足，通过单一的相似性度量很难选择合适的样本。为了解决这些问题，提出了一种基于多加权相似度度量的集成即时学习建模方法（MWS-EJITL）。首先，使用多加权相似性度量来选择相关样本。然后，构建局部模型并估计查询数据的输出值。最后，采用集成学习策略来整合每个局部模型的输出。以这个为基础，引入累积相似度因子优化局部建模的样本数，设置相似度阈值自适应更新局部模型。轧制力预测实验验证了MWS-EJITL方法的有效性和准确性。