Driving amount based stochastic configuration network for industrial process modeling

Abstract

Stochastic configuration network (SCN) that randomly assigns the weights connecting the input layer and the hidden layer with an inequality constraint can achieve a fast learning speed for dealing with regression tasks. In this paper, a driving amount based SCN (DASCN) is proposed to improve the performance in terms of generalization and structure compactness, which have gained considerable attention in the industrial process. In the proposed DASCN, the driving amount incorporated into SCN is used to further improve the structural parameters, especially the output weights, of SCN. The performance of DASCN is evaluated by function approximation, four benchmark datasets and practical application in the industrial process. The simulation results indicate that the DASCN has better generalization capability and a more compact network structure compared to other methods.