Chaotic attractor reconstruction using small reservoirs—the influence of topology

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lina Jaurigue
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引用次数: 0

Abstract

Forecasting timeseries based upon measured data is needed in a wide range of applications and has been the subject of extensive research. A particularly challenging task is the forecasting of timeseries generated by chaotic dynamics. In recent years reservoir computing has been shown to be an effective method of forecasting chaotic dynamics and reconstructing chaotic attractors from data. In this work strides are made toward smaller and lower complexity reservoirs with the goal of improved hardware implementability and more reliable production of adequate surrogate models. We show that a reservoir of uncoupled nodes more reliably produces long term timeseries predictions than more complex reservoir topologies. We then link the improved attractor reconstruction of the uncoupled reservoir with smaller spectral radii of the resulting surrogate systems. These results indicate that, the node degree plays an important role in determining whether the desired dynamics will be stable in the autonomous surrogate system which is attained via closed-loop operation of the trained reservoir. In terms of hardware implementability, uncoupled nodes would allow for greater freedom in the hardware architecture because no complex coupling setups are needed and because, for uncoupled nodes, the system response is equivalent for space and time multiplexing.
利用小型水库重构混沌吸引子--拓扑结构的影响
在广泛的应用中,需要根据测量数据对时序进行预测,这已成为广泛研究的主题。一项特别具有挑战性的任务是预测由混沌动力学产生的时序。近年来,储层计算已被证明是预测混沌动力学和从数据中重建混沌吸引子的有效方法。在这项工作中,我们正朝着更小、更低复杂度的蓄水池迈进,目标是提高硬件的可实施性,并更可靠地生成适当的代用模型。我们表明,与更复杂的储层拓扑结构相比,无耦合节点储层能更可靠地生成长期时间序列预测。然后,我们将无耦合水库的改进吸引子重构与由此产生的代用系统较小的频谱半径联系起来。这些结果表明,节点度在决定自主代理系统中预期动态是否稳定方面起着重要作用,而自主代理系统是通过训练水库的闭环运行实现的。就硬件的可实施性而言,无耦合节点将使硬件结构具有更大的自由度,因为不需要复杂的耦合设置,而且对于无耦合节点,系统响应在空间和时间多路复用方面是等效的。
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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