Evolutionary stochastic configuration networks for industrial data analytics

Abstract

Stochastic configuration network (SCN) with compact architecture is expected for data modeling. However, the hidden-node parameters (HNPs) randomly configured may result in a slow learning process due to the redundant nodes embedded in the model. To resolve this problem, an evolutionary SCN based on an improved differential evolution (DE) algorithm is proposed in this paper. Specifically, the improved DE reuses the assignment information of last hidden node to find an appropriate search scope for the current one; employs a space reduction method to seed a promising population in the scope; and develops a performance-aware scheme to adjust the scale factor of mutation operators. The proposed evolutionary SCNs are compared with other methods on six datasets and then applied for two real-world applications. Experimental results demonstrate that the proposed method obtains superior performance in terms of compactness and accuracy, with great potential for real-world data analysis.