Stochastic configuration networks with improved supervisory mechanism

The stochastic configuration networks (SCNs) have been shown to have great potential for developing fast learning models, making them especially suitable for industrial devices. To achieve low consumption during the SCNs training process, this paper proposes an improved version of SCNs (ISCN-III). First, guided by the function dictionary set and the objective function space, a dynamic hyperparameter is proposed to replace the nonnegative sequence of the supervisory mechanism. Second, based on the dynamic hyperparameters and function dictionary set, an improved supervisory mechanism is proposed to further reduce computational consumption. Finally, the improved supervisory mechanism with the dynamic hyperparameter proves the universal approximation property of ISCN-III. ISCN-III is compared with SCNs and its improved versions using six benchmark datasets and subsequently applied to a grinding process and a continuous stirred tank reactor. The experimental results prove that ISCN-III has the advantages of low computational cost and fast convergence speed.