To predict the future evolution of dynamical systems purely from observations of the past data is of great potential application. In this work, a new formulated paradigm of reservoir computing is proposed for achieving model-free predication for both low-dimensional and very large spatiotemporal chaotic systems. Compared with traditional reservoir computing models, it is more efficient in terms of predication length, training data set required and computational expense. By taking the Lorenz and Kuramoto-Sivashinsky equations as two classical examples of dynamical systems, numerical simulations are conducted, and the results show our model excels at predication tasks than the latest reservoir computing methods.

翻译：纯粹从以往数据的观察中预测动态系统的未来演变,具有巨大的潜在应用潜力。在这项工作中,提出了一个新的储油层计算模式,以实现低维和非常大的时空混乱系统的无模型预言。与传统的储油层计算模型相比,在预言长度、所需培训数据集和计算费用方面,它效率更高。通过将洛伦茨和仓本-西瓦申斯基方程式作为两个典型的动态系统典型例子,进行了数字模拟,结果显示我们的模型在预言任务方面比最新的储油层计算方法要出色。