Data modeling for quality prediction using improved orthogonal incremental random vector functional-link networks

Abstract

Innumerable complex industrial processes, such as blast furnace (BF) ironmaking process, greatly rely on some well-performing quality models to realize prediction and control of product quality. This paper proposes an improved orthogonal incremental random vector functional-link networks (I-OI-RVFLNs) algorithm with compact structure and high computational efficiency and applies it to quality modeling of BF ironmaking process. First, in order to improve the convergence speed of the basic I-RVFLNs, Schmidt orthogonalization method is introduced to orthogonalize the output vectors of the hidden layer nodes obtaining the least squares solution. Then, the proposed I-OI-RVFLNs algorithm further improves the network construction mode of the existing I-RVFLNs simplifying the network structure. To cut out the hidden nodes with smaller output weights, the number of hidden nodes of the proposed method is first fixed in advance and then the output weights of each hidden node are iteratively updated to approximate the network output. In this way, the problem of low computational efficiency caused by too many hidden nodes in basic I-RVFLNs can be avoided in the proposed algorithm. Lastly, data experiments using actual industrial data show that the proposed algorithm has much better performance in terms of convergence speed and computational efficiency than other compared algorithms.