  This paper introduces a novel approach to predicting periodic time series
using reservoir computing. The model is tailored to deliver precise forecasts
of rhythms, a crucial aspect for tasks such as generating musical rhythm.
Leveraging reservoir computing, our proposed method is ultimately oriented
towards predicting human perception of rhythm. Our network accurately predicts
rhythmic signals within the human frequency perception range. The model
architecture incorporates primary and intermediate neurons tasked with
capturing and transmitting rhythmic information. Two parameter matrices,
denoted as c and k, regulate the reservoir's overall dynamics. We propose a
loss function to adapt c post-training and introduce a dynamic selection (DS)
mechanism that adjusts $k$ to focus on areas with outstanding contributions.
Experimental results on a diverse test set showcase accurate predictions,
further improved through real-time tuning of the reservoir via c and k.
Comparative assessments highlight its superior performance compared to
conventional models.
