论文标题
锅炉NOx发射浓度预测的自动编码器 - 赋予学习机模型
Auto-Encoder-Extreme Learning Machine Model for Boiler NOx Emission Concentration Prediction
论文作者
论文摘要
提出了一种自动编码器（AE）极限学习机（ELM）-AE-ELM模型，以根据相互信息算法（MI），AE和ELM的组合来预测NOX发射浓度。首先，实用变量的重要性由MI算法计算，并分析了该机制以确定与NOX发射浓度相关的变量。然后，进一步分析了所选变量与NOX发射浓度之间的时间延迟相关性，以重建建模数据。随后，将AE应用于输入变量中的隐藏特征。最后，ELM算法建立了NOX发射浓度与深度特征之间的关系。实际数据的实验结果表明，与最先进的模型相比，提出的模型显示出有希望的性能。
An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical variables is computed by the MI algorithm, and the mechanism is analyzed to determine the variables related to the NOx emission concentration. Then, the time delay correlations between the selected variables and NOx emission concentration are further analyzed to reconstruct the modeling data. Subsequently, the AE is applied to extract hidden features within the input variables. Finally, an ELM algorithm establishes the relationship between the NOx emission concentration and deep features. The experimental results on practical data indicate that the proposed model shows promising performance compared to state-of-art models.
