Controlling Dynamics of Complex Systems: Nonlinear Techniques vs Reinforcement Learning

控制复杂系统的动力学：非线性技术与强化学习

• 批准号: 448911871
• 负责人: Professor Dr. Michael Rosenblum
• 金额: --
• 依托单位: Institut für Physik und Astronomie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/448911871?language=en
• 关键词: Controlling; Dynamics; Complex; Systems; Nonlinear

Complex nonlinear systems attract the attention of researchers from many different fields of contemporary science. One important phenomenon extensively studied theoretically and in experiments, is the emergence of a collective mode in oscillatory networks due to a synchronization transition. This collective mode can be either highly desirable or harmful. For example, it is hypothesized that excessive synchrony in neuronal networks is responsible for the emergence of pathological rhythms in epilepsies and in Parkinson’s disease. A major goal of the project is to develop techniques for synchronization control, combining feedback control with the modern sub-discipline of machine learning, the Reinforcement Learning. Having in mind a possible application to a medical technique, called Deep Brain Stimulation, we concentrate on control schemes based on the application of pulse action. Another area where nonlinearity is capable of affecting the problem both constructively and destructively is imaging. Controlling mode-mixing between noise and signal one can essentially enhance the image quality. Here, we plan to use the techniques for synchronization control to manipulate the spatial dynamics in an image-forming system.

复杂的非线性系统引起了当代科学许多不同领域的研究人员的关注。在理论和实验中被广泛研究的一个重要现象是，由于同步跃迁，振荡网络中出现了集体模。这种集体模式可能是非常可取的，也可能是有害的。例如，假设神经元网络的过度同步性是癫痫和帕金森病病理性节律出现的原因。该项目的一个主要目标是开发同步控制技术，将反馈控制与机器学习的现代子学科--强化学习相结合。考虑到一种名为脑深部刺激的医疗技术的可能应用，我们专注于基于脉冲动作应用的控制方案。非线性能够对问题产生建设性和破坏性影响的另一个领域是成像。控制噪声和信号之间的模式混频可以从根本上提高图像质量。在这里，我们计划使用同步控制技术来操纵成像系统中的空间动力学。