LAUSR

Production quality indices of complex industrial processes are usually hard to be measured in real time, which leads to unavailability of closed-loop operational optimization and control. Therefore, data-driven modeling techniques have been extensively employed to estimate production quality indices online. However, the conventional data-driven modeling methods often fail to achieve good performance because of interference from outliers. To solve the above-mentioned problem, this paper proposes an improved random vector functional link network (RVFLN) using a novel training method, which adopts a ridge regularized model with weighted factor for each training sample to evaluate the output weights. The robustness of the model has been achieved by employing a nonparametric kernel density estimation method to assign the weighted factors according to the training sample. To ensure the quality and computational load of the network in online applications, various online learning versions are presented according to the scope of data sampling. The improved RVFLN called robust regularized RVFLN has been validated using UCI, Statlib standard data sets, and an industrial grinding operation data. Results show that our proposed modeling technique perform favorably, and demonstrate its good potential for real world applications.