基于维度缩减的合金加料智能预测模型的研究

Research on dimension decrement-based alloy charging intelligent prediction model

合金加料预测模型作为炼钢精炼环节重要的工艺模型,其准确性对合金加料以及精炼环节的进一步自动化起着非常重要的作用。在实际数据的基础上,运用维度缩减同RBF神经网络相结合的方法建立合金加料智能预测模型,结果证明维度缩减后的合金加料模型具有较好的预测效果。

Since the alloy charging prediction model is used as an very important processing model for the steel refinement process its accuracy will play a very important role in further automation of the alloy charging and steel refinement processes. An alloy charging intelligent prediction model has been established on the bases of real data in combination of dimension decrement with the RBF nerve network. The result shows that the alloy charging model is of better predictive performance after dimension decrement.