In this MSc thesis, we study the performance of a delayed optoelectronic reservoir computer. We focus our atention on the system’s optimization through the addition of a second temporal delay. We find that the addition of this delay has a positive influence for some benchmark tasks while it remains neutral in other cases. In particular, we evaluate linear and nonlinear memory capacities as well as nonlinear time series prediction of a chaotic Mackey-Glass system and NARMA (Nonlinear Auto Regressive Moving Average Models) systems.
Master Thesis presentation
Miguel C. Soriano 971 17 13 14 Contact form