‘Symbiotic’ data-driven modelling for the accurate prediction of mechanical properties of alloy steels
‘Symbiotic’ data-driven modelling for the accurate prediction of mechanical properties of alloy steels
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“共生”数据驱动建模，用于准确预测合金钢的机械性能
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Y. Yang
中科院分区：
文献类型：
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作者：
Sidahmed Gaffour;M. Mahfouf;Y. Yang
提出了一种新的基于共生模型的优化策略。该系统结合了线性回归模型（LR），非线性迭代部分自适应最小二乘模型（NIPALS），神经网络模型与双环程序（NNDLP），自适应数值建模（神经模糊建模NF）和冶金知识，以提供有效的建模解决方案，并实现最佳的预测性能。作为最后一步，融合过程用于执行基于聚合算法和聚类方法的常规决策，该方法允许从一组竞争解决方案中系统地选择最终的最佳预测结果。所提出的方法，然后应用到具有挑战性的环境中的多维，非线性和稀疏的数据空间组成的机械性能的“低碳钢”，特别是拉伸强度（TS）和屈服强度（YS）在热轧工业过程。使用的数据集包含从热带钢轧机获得的机械性能的关键信息，它的结论是，开发的新的系统建模方法是能够提供更好的预测比每个单独的模型，即使在数据分布区域被认为是稀疏的。
A new optimal strategy based on symbiotic modelling is proposed. The system combines Linear Regression Model (LR), Non-Linear Iterative Partial Adaptive Least Square Model (NIPALS), Neural Network Model with double loop procedures (NNDLP), Adaptive Numeric Modelling (Neural-Fuzzy modeling NF) and metallurgical knowledge in order to provide effective modelling solutions and achieve an optimal prediction performance. As a final step a fusion procedure is used to perform a routine decision making based on aggregation algorithm and clustering method that allow to systematically select the final best prediction outcome from a set of competing solutions. The proposed methodology is then applied to the challenging environment of a multi-dimensional, non-linear and sparse data space consisting of mechanical properties of ‘Mild’ Steel in particular Tensile Strength (TS) and Yield Strength (YS) in hot-rolling industrial processes. Using a data set containing critical information on the mechanical properties obtained from a hot strip mill, it is concluded that the developed new systematic modelling approach is capable of providing better prediction than each individual model even in data distribution areas which are reckoned to be sparse.