Controlling dynamical systems to complex target states using machine
  learning: next-generation vs. classical reservoir computing
Alexander Haluszczynski Daniel Köglmayr Christoph Räth
Controlling nonlinear dynamical systems using machine learning allows to not
only drive systems into simple behavior like periodicity but also to more
complex arbitrary dynamics. For this, it is crucial that a machine learning
system can be trained to reproduce the target dynamics sufficiently well. On
the example of forcing a chaotic parametrization of the Lorenz system into
intermittent dynamics, we show first that classical reservoir computing excels
at this task. In a next step, we compare those results based on different
amounts of training data to an alternative setup, where next-generation
reservoir computing is used instead. It turns out that while delivering
comparable performance for usual amounts of training data, next-generation RC
significantly outperforms in situations where only very limited data is
available. This opens even further practical control applications in real world
problems where data is restricted.