Consideration of the output series generated by hysteresis reservoir computing

Reservoir computing is a machine learning model that is widely used for time series tasks due to its advantages of low cost and fast learning. However, conventional reservoir computing often do not achieve the memory capacity and nonlinearity required for the task. To solve this problem, we proposed hysteresis reservoir computing which conventional reservoir neurons are replaced by hysteresis neurons. The model generates various output sequences by changing their parameters. In addition, it has the potential to memorize time series because it presents complex periodic solutions. In this paper, we confirm the dynamics generated by changing the hysteresis reservoir computing parameters. The experimental results show that changing the parameters improves the learning ability and can represent specific series of data. This indicates an important dynamics in terms of memory capacity and the ability to represent.