Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks

Zhu, C; Zhang, J

doi:10.23919/IConAC.2019.8895070

Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks

Lookup NU author(s): Dr Changhao Zhu, Dr Jie Zhang ORCiD

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

The development of data-driven process models based on bootstrap aggregated deep belief networks (BAGDBN) is presented in this paper. In developing a BAGDBN model, the original data are replicated by using bootstrap resampling with replacement technique. The replications of original processes data are utilized for the developments of individual DBNs. These DBN models are combined to form BAGDBN. A BAGDBN model can give more robust and accurate estimations and predictions of chemical process variables compared with conventional deep belief networks (DBN). The effectiveness of this novel modelling approach is demonstrated using two application examples, inferential estimation of polymer melt index in an industrial polypropylene polymerization process and modelling a conic water tank.