Residual compensation extreme learning machine for regression

Extreme learning machine (ELM) was proposed for training single hidden layer feedforward neural networks (SLFNs), and can provide an efficient learning solution for regression problem. However, the prediction error of ELM is unavoidable due to its limited modeling capability, and the nonlinear and stochastic nature of the regression problem. In this paper, a novel ELM, residual compensation ELM (RC-ELM), is proposed for regression problem by employing a multilayer structure with the baseline layer for building the feature mapping between the input and the output, and the other layers for residual compensation layer by layer iteratively. Two real world applications, device-free localization (DFL) and gas utilization ratio (GUR) prediction in blast furnace, are used for experimental testing of the proposed RC-ELM. Experimental results show that RC-ELM has better generalization performance and robustness than other machine learning approaches, including the classic ELM, weighted K-nearest neighbor (WKNN), support vector machine (SVM), and back propagation neural network (BPNN).