A new online learning algorithm for structure-adjustable extreme learning machine

In actual industrial fields, data for modelling are usually generated gradually, which requires that the data-based prediction model has the online learning capability. Although many online learning algorithms have been proposed, the generalization performance needs to be improved further. In this paper, a structure-adjustable online learning neural network (SAO-ELM) based on the extreme learning machine (ELM) with quicker learning speed and better generalization performance is proposed. Firstly, ELM is changed into a structure-adjustable learning machine, in which the number of nodes in its single hidden layer can be adjusted. Then, a special strategy is developed to handle the difficulty that the new added hidden nodesʹ outputs corresponding to the discarded training data cannot be obtained. After that, an iterative equation is presented to update the output matrix when hidden nodes are added. Results of numerical comparison based on data from the real world benchmark problems and an actual continuous casting process show that the performance of SAO-ELM has significant advantages over that of the typical online learning algorithms on generalization performance. In addition, SAO-ELM retains the merit of quick learning characteristic of ELM.