RVFLN-based online adaptive semi-supervised learning algorithm with application to product quality estimation of industrial processes

Abstract

Direct online measurement on product quality of industrial processes is difficult to be realized, which leads to a large number of unlabeled samples in modeling data. Therefore, it needs to employ semi-supervised learning (SSL) method to establish the soft sensor model of product quality. Considering the slow time-varying characteristic of industrial processes, the model parameters should be updated smoothly. According to this characteristic, this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network (RVFLN), denoted as OAS-RVFLN. By introducing a L2-fusion term that can be seen a weight deviation constraint, the proposed algorithm unifies the offline and online learning, and achieves smoothness of model parameter update. Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy. Finally, the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product, which further verifies its effectiveness and potential of industrial application.