Optimal input selection for neural fuzzy modelling with application to Charpy energy prediction
Optimal input selection for neural fuzzy modelling with application to Charpy energy prediction
复制标题
应用于夏比能量预测的神经模糊建模的最佳输入选择
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Qian Zhang
中科院分区：
文献类型：
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作者：
Y. Yang;M. Mahfouf;Qian Zhang
输入变量的选择在数据驱动建模中起着关键作用，特别是对于输入/输出空间之间具有高维数的复杂系统。本文提出了一种新的基于人工神经网络的前向输入选择方案。该方案的目标是选择最少数量的重要变量作为模型输入，然后将用于神经模糊数据建模。建议的输入选择方案应用于夏比冲击能量预测的案例研究，从工业数据库中提取的数据。模型的性能进行了比较与以前的结果，其中使用了一个更大的输入集。仿真结果表明，可以显着减少输入的夏比数据模型的数量，几乎没有性能下降。此外，该方案的性能优于标准的相关分析和模糊聚类的输入选择方案
Input variables selection plays a critical role in data-driven modelling, especially for complex systems with high dimensionality between the input/output space. In this paper, a new artificial neural network based forward input selection scheme is proposed. The objective of the proposed scheme is to select the smallest number of important variables as model inputs, which will then be used for neural-fuzzy data modelling. The proposed input selection scheme is applied to a case study of Charpy impact energy prediction, with data extracted from an industrial database. Model performance has been compared with previous results where a much larger input set was used. Simulation results show that the number of inputs for the Charpy data model can be significantly reduced with little performance degradation. Also, the performance of the proposed scheme outperforms both the standard correlation analysis and fuzzy clustering based input selection schemes