Kernel Stochastic Configuration Networks for Industrial Data Regression

Stochastic configuration networks (SCNs) are featured by its way of random parameters assignment in the light of a supervisory mechanism, resulting in the universal approximation property at algorithmic level. This article presents a kernel version of SCNs, termed KSCNs, aiming to enhance model’s representation learning capability and performance stability. The random bases of a built SCN model can be used to span a reproducing kernel Hilbert space, followed by our proposed algorithm for constructing KSCNs. It is shown that the data distribution in the reconstructive space is favorable for regression solving and the proposed KSCNs hold the universal approximation property. Three industrial datasets are used in this study for performance evaluation. Experimental results with comparisons against existing solutions clearly demonstrate that the proposed KSCNs remarkably outperform the original SCNs and some typical kernel methods for resolving industrial data regression problems.