A reservoir computer (RC) is a recurrent neural network (RNN) framework that achieves computational efficiency where only readout layer training is required. Additionally, it effectively predicts nonlinear dynamical system tasks and has various applications. RC is effective for forecasting nonautonomous dynamical systems with gradual changes to the external drive amplitude. This study investigates the predictability of nonautonomous dynamical systems with rapid changes to the phase of the external drive. The forced Van der Pol equation was employed for the base model, implementing forecasting tasks with the RC. The study findings suggest that, despite hidden variables, a nonautonomous dynamical system with rapid changes to the phase of the external drive is predictable. Therefore, RC can offer better schedules for individual shift workers.

翻译：储层计算机（RC）是一种循环神经网络（RNN）框架，其计算效率体现在仅需训练读出层。此外，它能有效预测非线性动力系统任务并具有多种应用。RC对于外部驱动振幅缓慢变化的非自治动力系统具有良好预测能力。本研究探讨了外部驱动相位快速变化的非自治动力系统的可预测性。采用受迫范德波尔方程作为基础模型，利用RC执行预测任务。研究结果表明，尽管存在隐藏变量，外部驱动相位快速变化的非自治动力系统仍具有可预测性。因此，RC可为轮班工人提供更优的排班方案。