Exploring the non-linearity in empirical modelling of a steel system using statistical and neural network models
Exploring the non-linearity in empirical modelling of a steel system using statistical and neural network models
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使用统计和神经网络模型探索钢铁系统经验建模中的非线性
DOI：
10.1080/00207540600792465
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
S. Datta
中科院分区：
文献类型：
--
作者：
P. Das;S. Datta
金属的物理性能与其化学性质和操作中的其他几个轧制参数之间的关系在本质上往往非常复杂。非线性回归模型在模拟潜在机制方面发挥着非常重要的作用，前提是它是已知的。人工神经网络提供了广泛的通用和灵活的非线性回归模型。最常用的神经网络称为多层感知器，可以改变模型的复杂性，从简单的参数模型到高度灵活的非参数模型。在这项特定的工作中，基于行业的数据集用于学习和优化神经网络架构，使用一些众所周知的神经网络系统下的预测算法。分析的结果进行了比较，通过经验统计建模从其预测误差水平和材料科学的知识所取得的结果。
The relationship between the physical properties of metal is often very complex in nature with its chemistry and several other rolling parameters in operation. Non-linear regression models play a very important role in modelling the underlying mechanism, provided it is known. Artificial neural networks provide a wide class of general-purpose and flexible non-linear regression models. The most commonly used neural networks, called multi-layered perceptrons, can vary the complexity of the model from a simple parametric model to a highly flexible nonparametric model. In this particular work, an industry-based data set is used for learning and optimizing the neural network architecture using some well-known algorithms for prediction under neural-net systems. The outcome of the analysis is compared with the results achieved through empirical statistical modelling from its prediction error level and the knowledge of materials science.