利用机器学习控制非线性动力系统进入任意状态
Controlling nonlinear dynamical systems into arbitrary states using machine learning
摘要：
我们提出了一种新的和完全数据驱动的控制方案，它依赖于机器学习（ML）。利用最近开发的基于ML的复杂系统的预测能力，我们证明了非线性系统可以被迫留在来自任何初始状态的任意动态目标状态。我们概述了我们的方法使用的例子洛伦兹和罗斯勒系统，并显示这些系统如何可以非常准确地不仅周期性，而且例如间歇性和不同的混沌行为。这种高度灵活的控制方案对手头所需的数据量要求很少，我们简要讨论了从工程到医学的可能应用。
英文摘要：
We propose a novel and fully data driven control scheme which relies on machine learning (ML). Exploiting recently developed ML-based prediction capabilities of complex systems, we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states coming from any initial state. We outline our approach using the examples of the Lorenz and the Rössler system and show how these systems can very accurately be brought not only to periodic but also to e.g. intermittent and different chaotic behavior. Having this highly flexible control scheme with little demands on the amount of required data on hand, we briefly discuss possible applications that range from engineering to medicine.
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