机器学习制造的噪音诱导了节能同步
Jingdong Zhang1,2,3, Luan Yang2, Qunxi Zhu2,4,5
1School of Mathematical Sciences, SCMS, and SCAM, <a href="https://ror.org/013q1eq08">Fudan University</a>, Shanghai 200433, China.
Physical review. E
|August 20, 2024
概括
我们开发了一个机器学习框架来控制噪音并实现复杂系统中的同步. 该框架使用节能的人工噪声,通过物理和生物实例进行验证.
科学领域:
- 复杂的系统复杂的系统.
- 机器学习 机器学习
- 非线性动力学是一种非线性动力学.
背景情况:
- 噪音诱导的同步在自然和工程系统中很常见.
- 控制同步通常需要大量的能量投入.
- 现有的方法缺乏适应不同系统的适应性.
研究的目的:
- 为噪音控制器开发机器学习框架,以实现同步.
- 确定和解释拟议框架的节能特性.
- 证明框架在各种复杂系统中的有效性.
主要方法:
- 制定用于噪音控制的机器学习框架.
- 研究隐含的能量规范化现象.
- 严格阐明了底层的同步机制.
- 在现实的约束下,在各种物理和生物系统中进行测试.
主要成果:
- 该框架成功实现了复杂系统中的同步.
- 发现了一种隐含的能量调节现象,导致节能的人工噪音.
- 阐明了驱动节能同步的机制.
- 该框架在各种系统中展示了实际可行性和有效性.
结论:
- 开发的机器学习框架为噪音诱导的同步提供了一种节能方法.
- 这些发现为控制复杂系统中的同步提供了一个新的机制.
- 该框架的适应性使其适用于各种现实世界的应用.
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