Enhancing reservoir predictions of chaotic time series by incorporating delayed values of input and reservoir variables.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-05-01 DOI:10.1063/5.0258250
Luk Fleddermann, Sebastian Herzog, Ulrich Parlitz
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引用次数: 0

Abstract

Time series generated by chaotic dynamical systems can be effectively predicted using readouts from driven reservoir dynamics. In practical scenarios, however, only time series measurements with partial knowledge of the chaotic system's state are usually available. To address this aspect, we evaluate and compare the performance of reservoir computing in predicting time series under both conditions of complete and partial knowledge of the state. Our results show that memory improves the prediction accuracy only when the system state is partially known. For cases with partial state knowledge, we extend the mean prediction horizon by including delayed values of both the input and reservoir variables. To ensure the robustness of this result, we test it in systems with varying degrees of complexity. Finally, we show that the inclusion of delayed values can also facilitate the optimization of hyperparameters for predictions based on full knowledge of the system state.

通过结合输入和储层变量的延迟值来增强混沌时间序列的储层预测。
混沌动力系统产生的时间序列可以利用驱动油藏动力学的读数进行有效预测。然而,在实际情况下,通常只有部分了解混沌系统状态的时间序列测量是可用的。为了解决这一问题,我们评估和比较了油藏计算在完全和部分状态知识条件下预测时间序列的性能。我们的研究结果表明,只有当系统状态部分已知时,内存才能提高预测精度。对于具有部分状态知识的情况,我们通过同时包含输入变量和存储变量的延迟值来扩展平均预测范围。为了确保这个结果的健壮性,我们在不同复杂程度的系统中进行了测试。最后,我们证明了延迟值的包含也可以促进基于系统状态完全知识的超参数预测的优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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