论文标题
与复发性神经网络的混乱系统的长期预测
Long-term prediction of chaotic systems with recurrent neural networks
论文作者
论文摘要
储层计算系统是一类复发性神经网络，最近已被利用，用于基于模型的，基于数据的基于数据的预测，对各种混沌动力学系统的状态演变。预测范围证明，大约有六个Lyapunov时间。是否有可能将预测时间显着延长到迄今为止的成就之外？我们阐明了一个方案，该方案将依赖时间但稀疏的数据输入到储层计算中，并证明实际状态的如此罕见的“更新”实际上可以为多种混乱系统提供任意长时间的预测范围。基于时间同步理论的物理理解得到了发展。
Reservoir computing systems, a class of recurrent neural networks, have recently been exploited for model-free, data-based prediction of the state evolution of a variety of chaotic dynamical systems. The prediction horizon demonstrated has been about half dozen Lyapunov time. Is it possible to significantly extend the prediction time beyond what has been achieved so far? We articulate a scheme incorporating time-dependent but sparse data inputs into reservoir computing and demonstrate that such rare "updates" of the actual state practically enable an arbitrarily long prediction horizon for a variety of chaotic systems. A physical understanding based on the theory of temporal synchronization is developed.
