Forecasting the Forced Van der Pol Equation with Frequent Phase Shifts Using a Reservoir Computer

Sho Kuno, Hiroshi Kori
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Abstract

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.
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利用油藏计算机预测相位频繁变化的强制范德波尔方程
水库计算机(RC)是一种递归神经网络(RNN)框架,只需对读出层进行训练即可提高计算效率。此外,它还能有效预测非线性动态系统任务,并有多种应用。RC 对于预测外部驱动振幅渐变的非自主动态系统非常有效。本研究探讨了外部驱动相位快速变化的非自主动力系统的可预测性。基础模型采用了加强范德波尔方程,并使用 RC 执行预测任务。研究结果表明,尽管存在隐藏变量,但外部驱动相位快速变化的非自主动力系统是可预测的。因此,RC 可以为个体轮班工人提供更好的时间安排。
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