Reconstruction of bifurcation diagrams using an extreme learning machine with a pruning algorithm
Yoshitaka Itoh, Masaharu Adachi · 2017
We describe the reconstruction of bifurcation diagrams using an extreme learning machine with a pruning algorithm. We can reconstruct the bifurcation diagram from only some time-series data by using a neural network. However, the reconstruction accuracy is influenced by the structure of the neural network. To improve reconstruction accuracy we apply a pruning algorithm to the neural network used for the reconstruction of bifurcation diagrams. In this study, we use a pruned extreme learning machine (ELM) based on sensitivity analysis. In numerical experiments, first we compare time-series predictions using the ELM with and without the pruning algorithm. Then, we show the effectiveness of the pruned extreme learning machine for the reconstruction of bifurcation diagrams.