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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics
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Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics

机译：复杂性神经网络中复合时滞动力学预测的反向桥断算法和储层计算

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摘要

We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures. We highlight advantages and limitations of each method and discuss their implementation for parallel computing architectures. We quantify the relative prediction accuracy of these algorithms for the long-term forecasting of chaotic systems using as benchmarks the Lorenz-96 and the Kuramoto-Sivashinsky (KS) equations. We find that, when the full state dynamics are available for training, RC outperforms BPTT approaches in terms of predictive performance and in capturing of the long-term statistics, while at the same time requiring much less training time. However, in the case of reduced order data, large scale RC models can be unstable and more likely than the BPTT algorithms to diverge. In contrast, RNNs trained via BPTT show superior forecasting abilities and capture well the dynamics of reduced order systems. Furthermore, the present study quantifies for the first time the Lyapunov Spectrum of the KS equation with BPTT, achieving similar accuracy as RC. This study establishes that RNNs are a potent computational framework for the learning and forecasting of complex spatiotemporal systems. (c) 2020 Elsevier Ltd. All rights reserved.
机译：我们研究了经常性神经网络的效率，以预测使用储层计算（RC）以及通过时间（BPTT）进行储层网络架构的高维和减少订单复杂系统的时空动力学。我们突出了每个方法的优缺点，并讨论了他们对并行计算架构的实现。我们通过基准-96和Kuramoto-Sivashinsky（KS）方程来量化这些算法的相对预测准确性。我们发现，当全州动态可用于培训时，RC在预测性能和捕获长期统计数据方面胜过BPTT方法，同时需要更少的培训时间。然而，在减少订单数据的情况下，大规模的RC模型可能是不稳定的，并且比BPTT算法更有可能发散。相比之下，通过BPTT培训的RNNS显示出卓越的预测能力并捕获井下订单系统的动态。此外，本研究定量了KS方程的第一次与BPTT的Lyapunov光谱，实现了与RC相似的准确性。该研究确定RNN是对复杂时空系统的学习和预测的有效计算框架。 （c）2020 elestvier有限公司保留所有权利。

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