MI Weighted SDAE Pre-training Method for DNN and Its Application for Soft Sensing

Abstract

In modern industrial process, a lot of data about the process can be obtained, and data-driven soft sensor has been widely applied. One of them is deep neural networks (DNN) based soft sensor. Due to the large number of layers in DNN, the parameters of the networks are difficult to converge to a good value by back-propagation directly. This paper presented a novel mutual information (MI) weighted stacked denoising autoencoder (SDAE) method to find initial values of DNN. Compared with the traditional pre-training method like SDAE, the proposed method can obtain the parameters of DNN that can get features which have more MI with outputs. This method was applied to build soft sensor of debutanizer column. Experiment results indicate that when the dimension of deep features is selected properly, DNN pre-trained by MI weighted SDAE is more accurate than DNN pre-trained by SDAE and DNN without pre-training in terms of RMSE and r, thus verifying the feasibility and effectiveness of the proposed method.