Distributed parallel deep learning of Hierarchical Extreme Learning Machine for multimode quality prediction with big process data

Abstract

In this work, the distributed and parallel Extreme Learning Machine (dp-ELM) and Hierarchical Extreme Learning Machine (dp-HELM) are proposed for multimode process quality prediction with big data. The efficient ELM algorithm is transformed into the distributed and parallel modeling form according to the MapReduce framework. Since the deep learning network structure of HELM is more accurate than the single layer of ELM in feature representation, the dp-HELM is further developed through decomposing the ELM-based Auto-encoders (ELM-AE) of deep hidden layers into a loop of MapReduce jobs. Additionally, the multimode issue is solved through the “divide and rule” strategy. The distributed and parallel K-means (dp-K-means) is utilized to divide the process modes, which are further trained in a synchronous parallel way by dp-ELM and dp-HELM. Finally, the Bayesian model fusion technique is utilized to integrate the local models for online prediction. The proposed algorithms are deployed on a Hadoop MapReduce computing cluster and the feasibility and efficiency are illustrated through building a real industrial quality prediction model with big process data.