An Efficient Composite Deep Latent Feature Learning Framework With Layerwise Random Mapping for Complicated Industrial Soft Sensor Modeling

The soft sensor model based on deep neural networks (DNNs) can only extract strong nonlinear features from industrial data, and suffers from high training complexity and difficulties in determining the model structure. In order to deal with these issues, a composite deep partial least squares (CDPLSs) modeling method is proposed in this work to jointly extract both linear and nonlinear features hidden in process data. In this method, a hybrid latent variable learning framework is designed, where the linear partial least squares (PLSs) and nonlinear PLS are connected in a parallel way for capturing the linear and nonlinear latent features, respectively. Furthermore, for the efficient nonlinear feature mining in this framework, a multilayer random PLS model is constructed which applies a layerwise random Fourier feature (RFF) mapping to implement the deep nonlinear transformation. Finally, the superiority of this method is demonstrated through two industrial case studies.