论文标题
延迟嵌入式回声状态网络：部分观察到的系统的预测指标
Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems
论文作者
论文摘要
本文考虑了使用复发性神经网络对部分观察到的系统进行数据驱动预测的问题。尽管基于神经网络的动态预测因素在全州训练数据方面表现良好，但在训练阶段进行部分观察的预测却带来了重大挑战。在这里，使用回声状态网络（ESN）和部分观察到的状态的时间延迟嵌入，开发了部分观测的预测指标。所提出的方法在理论上是合理的，其嵌入定理和非线性系统的强可观察性是合理的。在三个系统上证明了该方法的功效：两个来自混乱动力学系统的合成数据集和一组实时流量数据。
This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial observation during training phase poses a significant challenge. Here a predictor for partial observations is developed using an echo-state network (ESN) and time delay embedding of the partially observed state. The proposed method is theoretically justified with Taken's embedding theorem and strong observability of a nonlinear system. The efficacy of the proposed method is demonstrated on three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.
