论文标题
混乱系统的转移学习
Transfer learning of chaotic systems
论文作者
论文摘要
通过系统a的时间序列训练的神经网络可以用于预测系统B的演变吗？这个问题是从广义上讲是转移学习，在机器学习和数据挖掘中非常重要，但尚未解决混乱系统。在这里，我们从基于同步的状态推理的角度研究了混乱系统的传输学习，在这种情况下，通过混沌系统A训练的储层计算机用于推断混乱系统B的未衡量变量，而A中A与B中的B不同。已经发现，如果系统A和B的参数不同，则可以很好地将储层计算机与系统B同步。但是，如果系统A和B的动力学不同，则储层计算机通常无法与System B同步。还研究了沿耦合储层计算机链条的知识传输，发现尽管储层计算机是通过不同系统培训的，但驱动系统的未测量变量可以通过远程储层计算机成功推断。最后，通过一个混乱的摆动的实验，我们表明，从建模系统中学到的知识可用于预测实验系统的演变。
Can a neural network trained by the time series of system A be used to predict the evolution of system B? This problem, knowing as transfer learning in a broad sense, is of great importance in machine learning and data mining, yet has not been addressed for chaotic systems. Here we investigate transfer learning of chaotic systems from the perspective of synchronization-based state inference, in which a reservoir computer trained by chaotic system A is used to infer the unmeasured variables of chaotic system B, while A is different from B in either parameter or dynamics. It is found that if systems A and B are different in parameter, the reservoir computer can be well synchronized to system B. However, if systems A and B are different in dynamics, the reservoir computer fails to synchronize with system B in general. Knowledge transfer along a chain of coupled reservoir computers is also studied, and it is found that, although the reservoir computers are trained by different systems, the unmeasured variables of the driving system can be successfully inferred by the remote reservoir computer. Finally, by an experiment of chaotic pendulum, we show that the knowledge learned from the modeling system can be used to predict the evolution of the experimental system.
