Online robust echo state broad learning system

Abstract Recurrent broad learning system (RBLS) is an effective learning method for processing sequential data. By replacing the enhancement nodes of the broad learning system with recurrent structure, RBLS obtains the capacity to capture the dynamic characteristics of time series data. However, RBLS is derived under the minimum mean square error (MMSE) criterion, which is sensitive to outliers. Moreover, RBLS is insufficient for online sequential learning. To address these limitations, we propose a novel online robust echo state structure based RBLS (OR-ESBLS). In OR-ESBLS, kernel recursive maximum correntropy (KRMC) is introduced to both enhance the robustness and discover the nonlinear characteristics of feature nodes in an online manner. To reduce the heavy computational requirements caused by the kernel method, a Quasi-Monte Carlo (QMC) based Random Fourier Feature (RFF) is utilized for kernel approximation. Furthermore, we adopt the randomized sparse reservoir as the enhancement nodes of RBLS, which can much more efficiently capture dynamic information of the data in the sequential learning setting. Experiments on both synthetic and real-world datasets are reported. The results show that the proposed OR-ESBLS can provide superior performance in online sequential time series prediction.