化工学报2016，Vol.67Issue(3)：729-735,7.DOI:10.11949/j.issn.0438-1157.20151847
改进型EMD-Elman神经网络在铁水硅含量预测中的应用
Application of improved EMD-Elman neural network to predict silicon content in hot metal
摘要
Abstract
To handle the multiscale and dynamic characteristics of blast furnace ironmaking process, a soft sensor model based on empirical mode decomposition (EMD) and Elman neural network is proposed. First, the original silicon content dataset is decomposed into a finite collection of intrinsic mode functions (IMFs) and a residue by EMD, obtaining relatively stationary sub-series from original data set. Second, each IMF and the residue are utilized to establish the corresponding Elman neural network model. To further improve the accuracy of prediction, the result of each sub-series is multiplied by a weight and then summed up to obtain the final silicon content. Here, all the weights are optimized by particle swarm optimization (PSO). The model was applied to the prediction of silicon content of blast furnace in a steel mill, and the result proved the effectiveness of the proposed method.关键词
硅含量/预测/多尺度/动态建模/经验模态分解/神经网络Key words
silicon content/prediction/multiscale/dynamic modeling/empirical mode decomposition/neural networks分类
矿业与冶金引用本文复制引用
宋菁华,杨春节,周哲,刘文辉,马淑艳..改进型EMD-Elman神经网络在铁水硅含量预测中的应用[J].化工学报,2016,67(3):729-735,7.基金项目
国家自然科学基金项目(61290321)；国家高技术研究发展计划项目(2012AA041709)。@@@@supported by the National Natural Science Foundation of China (61290321) and the National High Technology Research and Development Program of China (2012AA041709) （61290321）
