QR factorization based Incremental Extreme Learning Machine with growth of hidden nodes

In this paper, a computationally competitive incremental algorithm based on QR factorization is proposed, to automatically determine the number of hidden nodes in generalized single-hidden-layer feedforward networks (SLFNs). This approach, QR factorization based Incremental Extreme Learning Machine (QRI-ELM), is able to add random hidden nodes to SLFNs one by one. The computational complexity of this approach is analyzed in this paper as well. Simulation results show and verify that our new approach is fast and effective with good generalization and accuracy performance.