Multivariate Molten Iron Quality Modeling Based on Improved Incremental Random Vector Functional-link Networks

Abstract

Aiming at the problems that the model structure is complex and the training time is too long for traditional incremental random vector functional-link networks (I-RVFLNs), this paper proposes an improved incremental random vector functional-link networks (Improved I-RVFLNs). Compared with the traditional I-RVFLNs, this algorithm changes the mode of constructing network. First, the number of hidden layer nodes is fixed in advance, and then the output weights of all nodes are changed in sequence. Finally, based on the actual data of a large blast furnace in Southern China, a nonlinear auto-regressive exogenous (NARX) model of multivariate molten iron quality (MIQ) is established by using the improved I-RVFLNs, and then it is compared with other modeling algorithms. The results show that compared with other quality modeling algorithms, the proposed method not only has higher model accuracy and shorter test time, but also solves the problem that the number of optimal hidden layer nodes in the conventional RVFLNs algorithm is difficult to choose, and the network is too complicated due to too many hidden nodes in the traditional I-RVFLNs.