An improved stochastic configuration network for concentration prediction in wastewater treatment process

A learner model with fast learning and compact architecture is expected for industrial data modeling. To achieve these goals during stochastic configuration networks (SCNs) construction, we propose an improved version of SCNs in this paper. Unlike the original SCNs, the improved one employs a new inequality constraint in the construction process. In addition, to speed up the construction efficiency of SCNs, a node selection method is proposed to adaptively select nodes from a candidate pool. Moreover, to reduce the redundant nodes of the built SCNs model, we further compress the model based on the singular value decomposition algorithm. The improved SCNs are compared with other methods over four datasets and then applied to the ammonia–nitrogen concentration prediction task in the wastewater treatment process. Experimental results indicate that the proposed method has good potential for industrial data analytics.