Chaotic time series prediction using add-delete mechanism based regularized extreme learning machine

Abstract

Considering a regularized extreme learning machine (RELM) with randomly generated hidden nodes, an add-delete mechanism is proposed to determine the number of hidden nodes adaptively, where the extent of contribution to the objective function of RELM is treated as the criterion for judging each hidden node, that is, the large the better, and vice versa. As a result, the better hidden nodes are kept. On the contrary, the so-called worse hidden nodes are deleted. Naturally, the hidden nodes of RELM are selected optimally. In contrast to the other method only with the add mechanism, the proposed one has some advantages in the number of hidden nodes, generalization performance, and the real time. The experimental results on classical chaotic time series demonstrate the effectiveness and feasibility of the proposed add-delete mechanism for RELM.