Parametric Validation of the Reservoir Computing-Based Machine Learning Algorithm Applied to Lorenz System Reconstructed Dynamics

IF 0.7 4区 数学 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Samuele Mazzi, D. Zarzoso
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引用次数: 0

Abstract

A detailed parametric analysis is presented, where the recent method based on the reservoir computing paradigm, including its statistical robustness, is studied. It is observed that the prediction capabilities of the reservoir computing approach strongly depend on the random initialization of both the input and the reservoir layers. Special emphasis is put on finding the region in the hyperparameter space where the ensemble-averaged training and generalization errors together with their variance are minimized. The statistical analysis presented here is based on the projection on proper elements method.
基于油藏计算的机器学习算法在Lorenz系统重构动力学中的参数验证
提出了详细的参数分析,其中研究了基于油藏计算范式的最新方法,包括其统计稳健性。结果表明,储层计算方法的预测能力在很大程度上依赖于输入和储层的随机初始化。特别强调在超参数空间中寻找集平均训练误差和泛化误差及其方差最小的区域。本文提出的统计分析是基于适当元投影法。
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
Advances in Complex Systems
Advances in Complex Systems 综合性期刊-数学跨学科应用
CiteScore
1.40
自引率
0.00%
发文量
121
审稿时长
6-12 weeks
期刊介绍: Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.
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