A regularized stochastic configuration network based on weighted mean of vectors for regression

The stochastic configuration network (SCN) randomly configures the input weights and biases of hidden layers under a set of inequality constraints to guarantee its universal approximation property. The SCN has demonstrated great potential for fast and efficient data modeling. However, the prediction accuracy and convergence rate of SCN are frequently impacted by the parameter settings of the model. The weighted mean of vectors (INFO) is an innovative swarm intelligence optimization algorithm, with an optimization procedure consisting of three phases: updating rule, vector combining, and a local search. This article aimed at establishing a new regularized SCN based on the weighted mean of vectors (RSCN-INFO) to optimize its parameter selection and network structure. The regularization term that combines the ridge method with the residual error feedback was introduced into the objective function in order to dynamically adjust the training parameters. Meanwhile, INFO was employed to automatically explore an appropriate four-dimensional parameter vector for RSCN. The selected parameters may lead to a compact network architecture with a faster reduction of the network residual error. Simulation results over some benchmark datasets demonstrated that the proposed RSCN-INFO showed superior performance with respect to parameter setting, fast convergence, and network compactness compared with other contrast algorithms.

Automated summary

This article introduces a new regularized SCN model (RSCN-INFO) based on the weighted mean of vectors to optimize the parameter selection and network structure. The proposed model utilizes a regularization term and an automatic parameter search algorithm (INFO) to dynamically adjust the training parameters. Simulation results demonstrate that RSCN-INFO outperforms other contrast algorithms in terms of parameter setting, fast convergence, and network compactness.