平行堆栈式自编码器及其在过程建模中的应用

在面对流程工业存在的多参数、强非线性和富含复杂机理等问题时浅层算法的学习能力有限,故将深度学习理论引入过程工业预测建模中。而针对单个深层网络对多样性数据的特征挖掘困难,本文提出一种改进的堆栈式自编码器。该方法首先通过聚类算法对输入数据属性进行聚类,按结果将数据分类后输入并行的稀疏自编码器中进行特征的模块式提取,并行输出经整合后输入至叠加的深度网络中,联合这些特征再进行逐层学习得到拟合结果。为减轻过拟合带来的预测误差,将"dropout"方法引入网络训练中。在加氢裂化的预测建模研究中,所提出的算法具有比其他方法更好的预测水平和泛化能力。

Parallel stacked autoencoder and its application in process modeling

The problem of multiparameter, nonlinearity and complex mechanism in process industry limits the performance of shallow algorithm, and therefore the deep learning is introduced into the processing industry predictive modeling.However, feature mining is insufficient for diversity data by single deep network, so an improved stacked autoencoder is proposed.Firstly, attribute of input is divided into several classes by clustering algorithm, and then the parallel sparse autoencoder is entered to detect feature locally.The parallel output is integrated into the following deep networks to extract feature layer by layer and get the fitting results.To overcome predictive error due to the overfitting, the "dropout" technique is introduced.In the prediction modeling of hydrocracking, the presented algorithm has better prediction level and generalization ability.